From 99b1e96362c3a702574b083eba4c3eccfb683c18 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Sk=C3=B3rzewski?= <pawel.skorzewski@amu.edu.pl>
Date: Fri, 19 Mar 2021 09:08:11 +0100
Subject: [PATCH] =?UTF-8?q?Wyk=C5=82ady=201=20i=202?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 w1/zumz181.zip                        |   Bin 1214921 -> 0 bytes
 w1/zumz181a.ipynb                     |   774 -
 w1/zumz181a.pdf                       |   Bin 201645 -> 0 bytes
 w1/zumz181b.ipynb                     | 41701 --------------------
 w1/zumz181b.pdf                       |   Bin 734979 -> 0 bytes
 wyk/1_Wprowadzenie.ipynb              |   649 +
 wyk/2_Regresja_liniowa.ipynb          | 48213 ++++++++++++++++++++++++
 w1/data01.csv => wyk/data01_train.csv |    17 -
 w1/data02.tsv => wyk/data02_train.tsv |   335 -
 wyk/terms-cloud.png                   |   Bin 0 -> 29858 bytes
 wyk/venn-ai-ml-nn-dl-ds.png           |   Bin 0 -> 81279 bytes
 wyk/venn-ai-ml-nn-dl.png              |   Bin 0 -> 73808 bytes
 wyk/venn-ai-ml-nn.png                 |   Bin 0 -> 60979 bytes
 wyk/venn-ai-ml.png                    |   Bin 0 -> 40734 bytes
 wyk/venn-ai.png                       |   Bin 0 -> 23284 bytes
 15 files changed, 48862 insertions(+), 42827 deletions(-)
 delete mode 100644 w1/zumz181.zip
 delete mode 100644 w1/zumz181a.ipynb
 delete mode 100644 w1/zumz181a.pdf
 delete mode 100644 w1/zumz181b.ipynb
 delete mode 100644 w1/zumz181b.pdf
 create mode 100644 wyk/1_Wprowadzenie.ipynb
 create mode 100644 wyk/2_Regresja_liniowa.ipynb
 rename w1/data01.csv => wyk/data01_train.csv (82%)
 rename w1/data02.tsv => wyk/data02_train.tsv (79%)
 create mode 100644 wyk/terms-cloud.png
 create mode 100644 wyk/venn-ai-ml-nn-dl-ds.png
 create mode 100644 wyk/venn-ai-ml-nn-dl.png
 create mode 100644 wyk/venn-ai-ml-nn.png
 create mode 100644 wyk/venn-ai-ml.png
 create mode 100644 wyk/venn-ai.png

diff --git a/w1/zumz181.zip b/w1/zumz181.zip
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diff --git a/w1/zumz181a.ipynb b/w1/zumz181a.ipynb
deleted file mode 100644
index eecd2c3..0000000
--- a/w1/zumz181a.ipynb
+++ /dev/null
@@ -1,774 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## Uczenie maszynowe 2017/2018\n",
-    "# 1. Wprowadzenie. Regresja liniowa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## 1.1. Uczenie maszynowe – przykłady zastosowań"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Rozpoznawanie i rozumienie mowy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import IPython"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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4NpSba8jTWqR6ip+Fm7rhy7Ly8uEw05KlD8KHqVZ6alNCVqUPwoae1zi9CW5v\nV7dxZC0lYohOz1V1zGzLN2SC6yUllHptp2voZ4yd0WQn2grYm5ybb1QKTvuwUuzoJd37wHc5P7zB\nVGnrZiZkKBoco5dNzHiG5O+91yNEUnrcicI31LSPJlpIw4zglLFSdWjPq5PdWumezVoR6xtbDUqC\neiZNwslcs+jmJ+7Vpv4ivo9i72zUn739DsVh13lcqVpqN9zXdWe2OHrcOr0cTToSUXOe1mWy4JjI\nq+WLXhI9niNFR6Q4OLejjv8AE9OcUtORdSRxv7OxCdnFLzZuw2GjQp23b3Z7NahGfgY+ofWZE7+I\n1ZHn46C6lPxPLnue7xHDuGHcm9Dwp7llZ5FGW++wpKKhrvzFbZPuFCJTGu+4RDLvs9Shm33qxH52\nC3KysHv1QDJ//SLqe6Ttpy8PEoTt4Itp72/IDv8AoTXdLBVo83NfodLHGrrJ06l9O9HJdEZWwdTX\nXMjo6lR1JZmlc5X26z00Z6Sk3CEG2tE4lkKlKsssqcYvuaMKZMXzG1caalCjTknljff1bpoZV6a0\n6qHuRnc+z4IRO70LbqY2xxKS9Ve5FVXqMTZThFPlIqTtuDWZDaYinSoqTUlJ2bW5aqOHa2mv9xU+\nytxFWl1qTVo940xrVChyz28yz7Lh97P4mWVScJJNKz2kmXRxCa5DUwPA0E7wco+Qk8PTV/3s7kyq\nylsR+pctVW6WmlSXvJVJe1J+8dRciWsokTRTjaV8zfmzTFuxniy+LLyiasjLXYtumVQ1GEhcTK97\nJAt9SLg2iZion3szVXy5l03cpcLsmiq3gEd9UM1Z25BbQs9jXhYLK21zLKyXUVNF6r/QKFlSiTXa\n6ip+F/oavtymY+d0runDyRqqUssFrdiU6eSnDn2UNUdltuc3XWdt5rIuUNLtWKbNVLo1U5aOM+YV\nnjuPFdoZU2iaULpy9xBqpyvBXWgWs20FGMnFpLYZxa5BVbYpMlZkagNBtMaSfMVNWXeLWdptFESW\nqGp3zW2vzETUo3dx47oitLSvoyjEJwnBvnsW20uinEJzhfmgjx+kkJUZYXGxTbpT1NbqxqxjOOsZ\nK6ZdUpxxeFlh6uqkrHg0a1ThlZ4TFfw79iZfaeq9Zq5kfZxFy+M1OKlGSaezRXWpNrOnqiKz8Ts8\nFUOYqesdNjJKeCqd9tTmanrM3x9McvZSUQSjTIflsQT8dCAAlPzIJT8QJtysAW5ah+QRK095bS3X\nw8ylFtPf8ijveiNNVOG1FdKfWaN+R7SbTaas1o13HhdE/wDtsn/f/hHQN9ZZytfbNzOVdYCRQbCm\nUrbMLleayElVv6vxCL3NLzFUm9FexTHe5bGWoU6pu6YzjZ8kTKdnsKlmvdgRJxcowa3YTpQp1lGK\n2V27lVaLzImGVPR77muPtnl6aVtoWKKsIoqy1LLnSsRKGegsRr7mcXUaO3IsiV27SHLfKSrIse+m\nhQnYfMA90JKVlchu4s3eOhrjGeVz0hu6Fu02yafai1zRXIxz441w590NmvqwVmI9OY1FZqiS5jjP\nK87nGrOpXtS+Ilaj+5n25+q+Zr6tldam+pn+FnXueH7fJxVGvfD01bVRQ8mpxWvuMmHv1cPwotUs\np5n0DLsSvJX7iKlVZotK1xUs9978iJ2UNtQLlNd49HWEn3MywZZGTVTL3oK30KuS9uZf1yas1uYa\nehetUQPLJJ72FqUcuqaaIW26IcnYolUk92XVMJmpKdN9rmjPGTZdGq4xXJ+AGedJwTTWokUy7E1H\nOSb1YkW0RVyXZ3FyN3a1SLIWlEMrvowMdSm4yzRdjLi8NHEUnGtDMvHkeoorNcpxFLuehUcy+HYm\ni28HiLL2ZB13F4aOlCfw+p7Eo6iJZnoNMeFW/aFRSdSEacbdpLuPHqeuzssTTvSnp91nHVf4jNRi\nqySCTSJ/wQSQAEp+JBK91gJ8NSPHYnwICAsp7lRZTeoqu+6KL/0x/jf+D3E/LzPF6Kf9pv3zZ7Nm\nc3Q/kQ2lq3ZEJ2RMpuEeyBRWq8oNCq17rTvQkrt3BPQC9OwJty0K1LQZSfLcC9y0IztLRiypwcU1\nOWbuewW0AhN31e4845IRkhGranoU4RnQimtLHThGOdVUYTnHMti/qmlpqV0lKheL1hyfcXxqK250\nscu4iT7mSWxnfZg1Fq9iYuqovtDoEop7jLL3jF1Di7XIUS1KOXW4mZPYsTQJPRDycVbR35ldTVGo\nzaWj6zImtdSuM3Cqkt5ci2cXJX2J1Z4To+6qbtLXY0YFJ1G1skUOFtWW4erCje8krnLhLXXqcpJ5\negJX/gVPwv8AQpWMp+2viFXFU3Rn2l6r5l7KxOpx/LhMNKMqdJvWNlfKRVcetkoXtfS+55FGpUw8\nIqUsqlBSXiWyrQ/eTjWqOSUXBOPrPnfUz2undj1aEpUaiqRklJbDYiDjbM07pO8Xfc8ylj1OH7yL\nUlzK58Tna0Yq3iTGtejHROXIup2cIzuru6aPDWLq1J06dWclTlJXsuR6s8Ji+HTSrWlRl2ozXd32\n3Q7anc2wepoirux4b4rGD9VseHHot26qy783/BnGte1NK2m5XJ2Wx40+PQU9KTku9S/4IXSGm5Wd\nKStzuXE168Z2exdG04eKZ4cuN0rNqE/igp8dpKN8k83LZouLK9iasQncyrjOEqUW55lKK9n/AJMy\n41hk95/KTF17VP1SW2ebDjOHW+dX/sJfGcJf15d/qkw16JTXk1sYv23g09ajXnFjVMfh6sU4T320\nZcTSy3uQUvF0ntK/uZH2uF2ld27osirKkk4NPuOKrfxZeZ2E8RSafaRx9f8AjT82a4s8lZJBJplP\nvIAAAle6xBK91gJ8LkE+F9SAiB4biDQ3CvofRXTg8fGTPYPF6Lu3Bqbyyesto35nquo+UJ/KYbiz\nYrqS0Dro81JeaM9bEU72crIYupUmSnfR2t4lPX07+uiJYijH7yRBemrpPRkwu3a/vM32qjonUiPD\nEUX/ADVdAb796Ec7FSrQeuaPxIdWHKSfkBa530N+Bqqph7feg7M8lVE1c3RvTksTRV1JWqRXPxO3\nTjj1WxtvRq5EVllZD05xnFSg7pjZkdHIqkr6rUZvs6PcGlbYScFydguovZ7k9bmlqVuMl4idpPYu\nGtSq5XvdFfWq+hVd2d43K3mfIRGh1Ne1+Ys6qjG718FzK5Qla17k04pctTeMaMNJrEKrVWuyXca5\nOzdtuRixMurpp+JbScpUoyf3lc59b1rfSl7qaRkxCpyn2lF275I1Npc9DhMZ0ghPGVctObWd2d+R\njpcs8unV474dS6dH+n8JL6leIpUuoqWjNdl/eXd5nJft2F7ZKn5CVONwlCStU1TO/wB15/tMkH2I\n3XI9SvTpz4fhZxhKnUSkpNrSeuljPiKVGMMM6WZPqk5qS+8bEpVOEpwi5dVO909k/wDk8r1W/LFh\nsNVr1XGnFya1CvTtZKcZ3jfs8vAsi4Om1ltN7tEdSlS63Or3so2J4sVki2r3V1cuni5yil1lRyvr\nmfIWbjbxKJabE0wzdk5MrdRSioxSXuJl2oWvYJUVTtO97rdEVE7KOt7+CEnFO1lqy+8Jq7y3Iyq1\nvzKEpQcayglmLsilVk5JSjb4FUXKyyzd795dScYKV6vaZFiipaEtG7FL9bMkzXUoVqkM+mTvK4KV\nXstpKPNlRbDH5qfVuOWy9a+rKpLP2k7lbgpVNFdee5bDJRppuT0d7WJ6VXKF6qzVI2/tNal1SvKp\nmdtE1sZ6k1Vlns77aFas3a9rlRqdSMouzfuJp4t0W6dWUkuTMsrtZIq0VvZFNS8tW81vyM41a3Tl\nQdmqkr35O54tb+LLzNHvM9T12bZpCSCSoAAAIGX5CjL3AHvAACIGjuKNEK9/ASxEMFB0a00nfsrY\n20qsp08yxNSNR65c8jLw+Uf2dBJyzK/qhShGspKlLtR11ZlXpRxVV01kxOJSta7qX1K6mJxLp3jW\nrt8u2zCv4jXjraLYzq4ij2ErQXNaNDyrXDGYhZHLE4ictnFJldTEY2StDEVILnnla5XOU6dOEpyc\n29bp2LYLDVaketg4LZyzk0xVSx+Jp+vVzeEtfzNFKOMxUs0Kihrp2VZ/E34tcHhh4ulNTmkl2WYc\nRxBKnKEY3he67OqJb+DBUqY+lKSjVoVEl7KNeFnXnhE6zi5t/djbQ8uGNr4i1GFGm0n60ldo9SVT\nqaLjftRjuU+V8VUh2ZXtyZ6nDK6qUVkks0dGnz8DmMRjKlSzjeOmuvMejj5YeqsTS/3wvob4XL5T\nlx2OxjBxbnQ7PtQY8aim7O6l7L3MnDuI0cdSU4StK2qe6NrgprtJM9Dz+krfR38wqvllysXI1s7r\nuYk1K/NBNNC8na6XmTG19WVqU4t6J8tQjd7xa8gLoxcoyy7Ipej3Hi2k/wB235lbjNvXKvNhYmLV\ny1LmVxjGL5yl4IitCVRXqyy0/ZXMsqVlxdaNVtRTlSg+3KKvfwQ9LHwq2UM/k6bQ+DnGVSagrRis\nsY/qZ8dgHbraNTq5R9WSdrea5mOpF6fLbYq4hXqwapzcY06sWrvRr37HBYjC9TiJ05JvK7N7nb4d\nPiEsuJrOp1b9RwSueX0l4VCnGOKpQcIerJJ6Iz2zG9u+XMOFJP1c3vEmoxi9En3GqTybS0M+RyzS\nsnpzZMXXucRpdS6arN9ZkSce7RWN/B6lD7JisNVcYKpRbjJ+0tUjk6vHMVVo0qco0v3ayqSjrJeL\n5hS45iqTeWNF3Vu1C5jcq54b5Nd9i6bSwsW4tye7at8O88T9pV3vlfuGrcXxdeKjVkpKOivfQjTZ\nKauQ5pvXY82WLqS5R+BKxtR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-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"600\"\n",
-       "            src=\"https://www.youtube.com/embed/qGU-SqUTees\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679e10>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IPython.display.YouTubeVideo('qGU-SqUTees', width=800, height=600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"600\"\n",
-       "            src=\"https://www.youtube.com/embed/UlZtr9fjQcU\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679bd0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IPython.display.YouTubeVideo('UlZtr9fjQcU', width=800, height=600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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F8nEA51ocJsTbyQo6T0Mj6Dmhn3GuUv4tSehiFBzYyNG/GLfkHs7vja+dySQrBDGT4lkp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\nQhAAhCEAP//Z\n",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"600\"\n",
-       "            src=\"https://www.youtube.com/embed/P18EdAKuC1U\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679b90>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IPython.display.YouTubeVideo('P18EdAKuC1U', width=800, height=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Rozpoznawanie obrazów"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "<img style=\"margin: auto\" width=\"60%\" src=\"https://chaosmail.github.io/images/deep-learning/recognition.png\"/>\n",
-    "\n",
-    "<sub>Źródło: Christoph Körner, https://chaosmail.github.io/deeplearning/2016/10/22/intro-to-deep-learning-for-computer-vision/</sub>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "<img style=\"margin: auto\" width=\"60%\" src=\"https://chaosmail.github.io/images/deep-learning/classification.png\"/>\n",
-    "\n",
-    "<sub>Źródło: Christoph Körner, https://chaosmail.github.io/deeplearning/2016/10/22/intro-to-deep-learning-for-computer-vision/</sub>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true,
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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Xv3rWPaXp1JqYbR4cZJ8borzwmPnXWjl5vdcLG18rnHC2LgNVMCc/0wRrktsMQnkwO+Td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\nEBERAREQEREH/9k=\n",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"600\"\n",
-       "            src=\"https://www.youtube.com/embed/acKTCF0sAfc\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679e50>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IPython.display.YouTubeVideo('acKTCF0sAfc', width=800, height=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "<img style=\"margin: auto\" width=\"60%\" src=\"https://static.techspot.com/images2/news/bigimage/2014/11/2014-11-18-image-10.jpg\"/>\n",
-    "\n",
-    "<sub>Źródło: https://www.techspot.com/news/58872-google-new-image-recognition-software-can-describe-entire.html</sub>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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Yd07FUebjmrlNNXNncwQQzxXYltX+GVAcA+RHgaX93ysm6cs4TeoHj5inNJc2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\nGJKt3s9/n/2qzXJ2e8CseEAO0dPWupbNGxMBjkWUbtjjH4G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-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"600\"\n",
-       "            src=\"https://www.youtube.com/embed/8BFzu9m52sc\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679290>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IPython.display.YouTubeVideo('8BFzu9m52sc', width=800, height=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Tłumaczenie maszynowe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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UjbiMRZlWRWMYDHByNEoGlsg4I/hUS6V3o+O8IuFBGJ5bds7MBKiKAwH5xPtq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\nUUUUAUUUUAUUUUAUUUUAUUUUAUUUUAUUUUAUUUUB/9k=\n",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"600\"\n",
-       "            src=\"https://www.youtube.com/embed/06olHmcJjS0\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ffb085d9850>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IPython.display.YouTubeVideo('06olHmcJjS0', width=800, height=600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"600\"\n",
-       "            src=\"https://www.youtube.com/embed/rek3jjbYRLo\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ffb085d9b90>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IPython.display.YouTubeVideo('rek3jjbYRLo', width=800, height=600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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OLS7ePoX2aGyn4v6/XBqqb6szoTK/6hP+GfgmQ1lNHC1r5mNcBqCVzrqymfRT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\nqDhVL3JRNY7x3cJ3Spt1Yux4981OxtDlU19VM2q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-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"600\"\n",
-       "            src=\"https://www.youtube.com/embed/_GdSC1Z1Kzs\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ffb085d9d90>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IPython.display.YouTubeVideo('_GdSC1Z1Kzs', width=800, height=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Inne zastosowania"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "* systemy rekomendacyjne\n",
-    "* detekcja spamu\n",
-    "* klasyfikacja dokumentów/obrazów\n",
-    "* analiza nastrojów\n",
-    "* rozpoznawanie pisma odręcznego\n",
-    "* samochody autonomiczne\n",
-    "* przewidywanie kursów giełdowych\n",
-    "* automatyczna diagnostyka medyczna\n",
-    "* analiza genów\n",
-    "* sztuczna inteligencja w grach\n",
-    "* ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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FxzGFa8o3JYMg9DKwFS2tyAwLEES1wGGCJnUkvhtwDt8TKw/8RzY7msmy5ilyFSOu3/v4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\nTgS5qXRckAD5yzQ300XF7AS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-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"800\"\n",
-       "            height=\"600\"\n",
-       "            src=\"https://www.youtube.com/embed/SUbqykXVx0A\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ffb08679590>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IPython.display.YouTubeVideo('SUbqykXVx0A', width=800, height=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## 1.2. Czym jest uczenie maszynowe?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "<img style=\"float: right;\" src=\"https://upload.wikimedia.org/wikipedia/commons/f/f8/This_is_the_photo_of_Arthur_Samuel.jpg\"/>\n",
-    "\n",
-    "> Uczenie maszynowe to dziedzina nauki,\n",
-    "> która daje komputerom umiejętność uczenia się\n",
-    "> bez programowania ich _explicite_.\n",
-    "\n",
-    "> &mdash; <cite>Arthur Samuel, 1959</cite>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "<img style=\"float: right;\" width=\"20%\" src=\"http://mediad.publicbroadcasting.net/p/wamc/files/styles/x_large/public/201401/tom_mitchell.jpg\"/>\n",
-    "\n",
-    "> Mówimy, że program komputerowy **uczy się**\n",
-    "> z doświadczenia E w odniesieniu do zadania T i miary skuteczności P,\n",
-    "> jeżeli jego skuteczność wykonywania zadania T mierzona według P\n",
-    "> wzrasta z doświadczeniem E.\n",
-    "\n",
-    "> &mdash; <cite>Tom Mitchell, 1998</cite>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "<img style=\"margin: auto\" width=\"40%\" src=\"https://static1.squarespace.com/static/5150aec6e4b0e340ec52710a/t/51525c33e4b0b3e0d10f77ab/1364352052403/Data_Science_VD.png\"/>\n",
-    "\n",
-    "<sub>Źródło: Drew Conway, http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram</sub>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "Uczenie maszynowe to:\n",
-    "\n",
-    "* doskonalenie działania dla pewnych zadań na podstawie doświadczenia"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "* tworzenie systemów, które doskonalą swoje działania na podstawie przeszłych doświadczeń"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "* zestaw metod, które potrafią w sposób automatyczny wykrywać wzorce w danych, a następnie używać wcześniej niezaobserwowanych wzorców do przewidywania przyszłych zjawisk"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "Charakterystyczne cechy uczenia maszynowego:\n",
-    "\n",
-    "* „automatyzacja automatyzacji”\n",
-    "* komputer „sam się programuje”\n",
-    "* modelowanie danych zastępuje pisanie programu"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "<img style=\"margin: auto\" width=\"80%\" src=\"https://recast.ai/blog/wp-content/uploads/2017/02/image20.png\"/>\n",
-    "\n",
-    "<sub>Źródło: https://recast.ai/blog/machine-learning-algorithms/</sub>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## 1.3. Metody uczenia maszynowego"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Z jakimi rodzajami zadań mamy do czynienia?\n",
-    "\n",
-    "* Uczenie nadzorowane\n",
-    "  * Regresja\n",
-    "  * Klasyfikacja\n",
-    "* Uczenie nienadzorowane\n",
-    "  * Klastrowanie\n",
-    "* Uczenie przez wzmacnianie\n",
-    "* Systemy rekomendacyjne"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Klasyfikator\n",
-    "\n",
-    "* Klasyfikator to funkcja $h$, która przykładowi $x$ przyporządkowuje prognozowaną wartość $h(x)$.\n",
-    "* Jeżeli funkcja $h$ jest ciągła, to mówimy o zagadnieniu **regresji**.\n",
-    "* Jeżeli funkcja $h$ jest dyskretna, to mówimy o zagadnieniu **klasyfikacji**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Algorytm uczący\n",
-    "\n",
-    "* Dane są przykładowe obserwacje $(X, y)$.\n",
-    "* Staramy się dobrać funkcję (klasyfikator) $h$ tak, żeby $h(x) \\sim y$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "W jaki sposób można określić, czy klasyfikator jest „dobry”?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Podział metod uczenia maszynowego\n",
-    "\n",
-    "> \\[Każdy algorytm uczenia maszynowego\\] stanowi kombinację dokładnie trzech składników.\n",
-    "> Te składniki to:\n",
-    "> * reprezentacja\n",
-    "> * ewaluacja\n",
-    "> * optymalizacja\n",
-    "\n",
-    "> &mdash; Pedro Domingos, “A Few Useful Things to Know about Machine Learning”"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Reprezentacja\n",
-    "\n",
-    "* drzewa decyzyjne\n",
-    "* regresja liniowa\n",
-    "* regresja logistyczna\n",
-    "* naiwny klasyfikator bayesowski\n",
-    "* algorytm $k$ najbliższych sąsiadów\n",
-    "* sieci neuronowe\n",
-    "* maszyny wektorów nośnych\n",
-    "* algorytmy genetyczne\n",
-    "* ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Ewaluacja\n",
-    "\n",
-    "* skuteczność (dokładność)\n",
-    "* precyzja i pokrycie\n",
-    "* błąd średniokwadratowy\n",
-    "* _information gain_\n",
-    "* _logistic loss_\n",
-    "* BLEU\n",
-    "* ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Optymalizacja\n",
-    "\n",
-    "* optymalizacja kombinatoryczna:\n",
-    "  * wyszukiwanie zachłanne,\n",
-    "  * _beam search_...\n",
-    "* optymalizacja ciągła:\n",
-    "  * nieograniczona:\n",
-    "    * metoda gradientu prostego,\n",
-    "    * metoda Newtona...\n",
-    "  * ograniczona:\n",
-    "    * programowanie liniowe..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "celltoolbar": "Slideshow",
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  },
-  "livereveal": {
-   "start_slideshow_at": "selected",
-   "theme": "amu"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/w1/zumz181a.pdf b/w1/zumz181a.pdf
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diff --git a/w1/zumz181b.ipynb b/w1/zumz181b.ipynb
deleted file mode 100644
index 356c287..0000000
--- a/w1/zumz181b.ipynb
+++ /dev/null
@@ -1,41701 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## Uczenie maszynowe UMZ 2017/2018\n",
-    "# 1. Wprowadzenie. Regresja liniowa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## 1.4 Funkcja kosztu"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Zadanie\n",
-    "Znając $x$ – ludność miasta (w dziesiątkach tysięcy mieszkańców),\n",
-    "należy przewidzieć $y$ – dochód firmy transportowej (w dziesiątkach tysięcy dolarów).\n",
-    "\n",
-    "(Dane pochodzą z kursu „Machine Learning”, Andrew Ng, Coursera)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Nagłowki, można zignorować\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as pl\n",
-    "import ipywidgets as widgets\n",
-    "\n",
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "\n",
-    "from IPython.display import display, Math, Latex"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Dane"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6.1101,17.592\n",
-      "\n",
-      "5.5277,9.1302\n",
-      "\n",
-      "8.5186,13.662\n",
-      "\n",
-      "7.0032,11.854\n",
-      "\n",
-      "5.8598,6.8233\n",
-      "\n",
-      "8.3829,11.886\n",
-      "\n",
-      "7.4764,4.3483\n",
-      "\n",
-      "8.5781,12\n",
-      "\n",
-      "6.4862,6.5987\n",
-      "\n",
-      "5.0546,3.8166\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open('data01.csv') as data:\n",
-    "    for line in data.readlines()[:10]:\n",
-    "        print(line)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Wczytanie danych"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x = [6.1101, 5.5277, 8.5186, 7.0032, 5.8598, 8.3829, 7.4764, 8.5781, 6.4862, 5.0546]\n",
-      "y = [17.592, 9.1302, 13.662, 11.854, 6.8233, 11.886, 4.3483, 12.0, 6.5987, 3.8166]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import csv\n",
-    "\n",
-    "reader = csv.reader(open('data01.csv'), delimiter=',')\n",
-    "\n",
-    "x = list()\n",
-    "y = list()\n",
-    "for xi, yi in reader:\n",
-    "    x.append(float(xi))\n",
-    "    y.append(float(yi)) \n",
-    "    \n",
-    "print('x = {}'.format(x[:10])) \n",
-    "print('y = {}'.format(y[:10]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Hipoteza i parametry modelu"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "$$ h_{\\theta}(x) = \\theta_0 + \\theta_1 x $$\n",
-    "$$ \\theta = \\left[\\begin{array}{c}\\theta_0\\\\ \\theta_1\\end{array}\\right] $$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
-       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
-       "<!-- Created with matplotlib (http://matplotlib.org/) -->\n",
-       "<svg height=\"360pt\" version=\"1.1\" viewBox=\"0 0 662 360\" width=\"662pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-       " <defs>\n",
-       "  <style type=\"text/css\">\n",
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-       "z\n",
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-       "  </g>\n",
-       "  <g id=\"axes_1\">\n",
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-    "# Funkcje rysujące wykres kropkowy oraz prostą regresyjną\n",
-    "\n",
-    "def regdots(x, y):    \n",
-    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
-    "    ax.scatter(x, y, c='r', s=50, label='Dane')\n",
-    "    \n",
-    "    ax.set_xlabel(u'Wielkość miejscowości [dzies. tys. mieszk.]')\n",
-    "    ax.set_ylabel(u'Dochód firmy [dzies. tys. dolarów]')\n",
-    "    ax.margins(.05, .05)\n",
-    "    pl.ylim(min(y) - 1, max(y) + 1)\n",
-    "    pl.xlim(min(x) - 1, max(x) + 1)\n",
-    "    return fig\n",
-    "\n",
-    "def regline(fig, fun, theta, x):\n",
-    "    ax = fig.axes[0]\n",
-    "    x0, x1 = min(x), max(x)\n",
-    "    X = [x0, x1]\n",
-    "    Y = [fun(theta, x) for x in X]\n",
-    "    ax.plot(X, Y, linewidth='2',\n",
-    "            label=(r'$y={theta0}{op}{theta1}x$'.format(\n",
-    "                theta0=theta[0],\n",
-    "                theta1=(theta[1] if theta[1] >= 0 else -theta[1]),\n",
-    "                op='+' if theta[1] >= 0 else '-')))\n",
-    "\n",
-    "def legend(fig):\n",
-    "    ax = fig.axes[0]\n",
-    "    handles, labels = ax.get_legend_handles_labels()\n",
-    "    # try-except block is a fix for a bug in Poly3DCollection\n",
-    "    try:\n",
-    "        fig.legend(handles, labels, fontsize='15', loc='lower right')\n",
-    "    except AttributeError:\n",
-    "        pass\n",
-    "\n",
-    "fig = regdots(x,y)\n",
-    "legend(fig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Hipoteza: funkcja liniowa jednej zmiennej\n",
-    "\n",
-    "def h(theta, x):\n",
-    "    return theta[0] + theta[1] * x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Przygotowanie interaktywnego wykresu\n",
-    "\n",
-    "sliderTheta01 = widgets.FloatSlider(min=-10, max=10, step=0.1, value=0, description=r'$\\theta_0$', width=300)\n",
-    "sliderTheta11 = widgets.FloatSlider(min=-5, max=5, step=0.1, value=0, description=r'$\\theta_1$', width=300)\n",
-    "\n",
-    "def slide1(theta0, theta1):\n",
-    "    fig = regdots(x, y)\n",
-    "    regline(fig, h, [theta0, theta1], x)\n",
-    "    legend(fig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "58798d3c3e2c4f14b9fc02d62f47f1da",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='$\\\\theta_0$', max=10.0, min=-10.0), FloatSlider(value=0.0, description='$\\\\theta_1$', max=5.0, min=-5.0), Button(description='Run Interact', style=ButtonStyle()), Output()), _dom_classes=('widget-interact',))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.slide1>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "widgets.interact_manual(slide1, theta0=sliderTheta01, theta1=sliderTheta11)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Funkcja kosztu"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "Będziemy szukać takich parametrów $\\theta = \\left[\\begin{array}{c}\\theta_0\\\\ \\theta_1\\end{array}\\right]$, które minimalizują fukcję kosztu $J(\\theta)$:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "$$ \\hat\\theta = \\mathop{\\arg\\min}_{\\theta} J(\\theta) $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "$$ \\theta \\in \\mathbb{R}^2, \\quad J \\colon \\mathbb{R}^2 \\to \\mathbb{R} $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Błąd średniokwadratowy\n",
-    "#### (metoda najmniejszych kwadratów)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "$$ J(\\theta) \\, = \\, \\frac{1}{2m} \\sum_{i = 1}^{m} \\left( h_{\\theta} \\left( x^{(i)} \\right) - y^{(i)} \\right) ^2 $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "$$ J(\\theta_0, \\theta_1) \\, = \\, \\frac{1}{2m} \\sum_{i = 1}^{m} \\left( \\theta_0 + \\theta_1 x^{(i)} - y^{(i)} \\right) ^2 $$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def J(h, theta, x, y):\n",
-    "    m = len(y)\n",
-    "    return 1.0 / (2 * m) * sum((h(theta, x[i]) - y[i])**2 for i in range(m))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Oblicz wartość funkcji kosztu i pokaż na wykresie\n",
-    "\n",
-    "def regline(fig, fun, theta, x, y):\n",
-    "    ax = fig.axes[0]\n",
-    "    x0, x1 = min(x), max(x)\n",
-    "    X = [x0, x1]\n",
-    "    Y = [fun(theta, x) for x in X]\n",
-    "    cost = J(fun, theta, x, y)\n",
-    "    ax.plot(X, Y, linewidth='2',\n",
-    "            label=(r'$y={theta0}{op}{theta1}x, \\; J(\\theta)={cost:.3}$'.format(\n",
-    "                theta0=theta[0],\n",
-    "                theta1=(theta[1] if theta[1] >= 0 else -theta[1]),\n",
-    "                op='+' if theta[1] >= 0 else '-',\n",
-    "                cost=cost)))\n",
-    "\n",
-    "sliderTheta02 = widgets.FloatSlider(min=-10, max=10, step=0.1, value=0, description=r'$\\theta_0$', width=300)\n",
-    "sliderTheta12 = widgets.FloatSlider(min=-5, max=5, step=0.1, value=0, description=r'$\\theta_1$', width=300)\n",
-    "\n",
-    "def slide2(theta0, theta1):\n",
-    "    fig = regdots(x, y)\n",
-    "    regline(fig, h, [theta0, theta1], x, y)\n",
-    "    legend(fig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c380a0076fb4480a9f9a0eb4f5fba756",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='$\\\\theta_0$', max=10.0, min=-10.0), FloatSlider(value=0.0, description='$\\\\theta_1$', max=5.0, min=-5.0), Button(description='Run Interact', style=ButtonStyle()), Output()), _dom_classes=('widget-interact',))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.slide2>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "widgets.interact_manual(slide2, theta0=sliderTheta02, theta1=sliderTheta12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Funkcja kosztu jako funkcja zmiennej $\\theta$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Wykres funkcji kosztu dla ustalonego theta_1=1.0\n",
-    "\n",
-    "def costfun(fun, x, y):\n",
-    "    return lambda theta: J(fun, theta, x, y)\n",
-    "\n",
-    "def costplot(hypothesis, x, y, theta1=1.0):\n",
-    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
-    "    ax.set_xlabel(r'$\\theta_0$')\n",
-    "    ax.set_ylabel(r'$J(\\theta)$')\n",
-    "    j = costfun(hypothesis, x, y)\n",
-    "    fun = lambda theta0: j([theta0, theta1])\n",
-    "    X = np.arange(-10, 10, 0.1)\n",
-    "    Y = [fun(x) for x in X]\n",
-    "    ax.plot(X, Y, linewidth='2', label=(r'$J(\\theta_0, {theta1})$'.format(theta1=theta1)))\n",
-    "    return fig\n",
-    "\n",
-    "def slide3(theta1):\n",
-    "    fig = costplot(h, x, y, theta1)\n",
-    "    legend(fig)\n",
-    "\n",
-    "sliderTheta13 = widgets.FloatSlider(min=-5, max=5, step=0.1, value=1.0, description=r'$\\theta_1$', width=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1b675985144a43d0997c79ada18bb446",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=1.0, description='$\\\\theta_1$', max=5.0, min=-5.0), Button(description='Run Interact', style=ButtonStyle()), Output()), _dom_classes=('widget-interact',))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.slide3>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "widgets.interact_manual(slide3, theta1=sliderTheta13)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Wykres funkcji kosztu względem theta_0 i theta_1\n",
-    "\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "import pylab\n",
-    "\n",
-    "%matplotlib inline\n",
-    "\n",
-    "def costplot3d(hypothesis, x, y, show_gradient=False):\n",
-    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
-    "    ax = fig.add_subplot(111, projection='3d')\n",
-    "    fig.subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0)\n",
-    "    ax.set_xlabel(r'$\\theta_0$')\n",
-    "    ax.set_ylabel(r'$\\theta_1$')\n",
-    "    ax.set_zlabel(r'$J(\\theta)$')\n",
-    "    \n",
-    "    j = lambda theta0, theta1: costfun(hypothesis, x, y)([theta0, theta1])\n",
-    "    X = np.arange(-10, 10.1, 0.1)\n",
-    "    Y = np.arange(-1, 4.1, 0.1)\n",
-    "    X, Y = np.meshgrid(X, Y)\n",
-    "    Z = np.matrix([[J(hypothesis, [theta0, theta1], x, y) \n",
-    "                    for theta0, theta1 in zip(xRow, yRow)] \n",
-    "                   for xRow, yRow in zip(X, Y)])\n",
-    "    \n",
-    "    ax.plot_surface(X, Y, Z, rstride=2, cstride=8, linewidth=0.5,\n",
-    "                    alpha=0.5, cmap='jet', zorder=0,\n",
-    "                    label=r\"$J(\\theta)$\")\n",
-    "    ax.view_init(elev=20., azim=-150)\n",
-    "\n",
-    "    ax.set_xlim3d(-10, 10);\n",
-    "    ax.set_ylim3d(-1, 4);\n",
-    "    ax.set_zlim3d(-100, 800);\n",
-    "\n",
-    "    N = range(0, 800, 20)\n",
-    "    pl.contour(X, Y, Z, N, zdir='z', offset=-100, cmap='coolwarm', alpha=1)\n",
-    "    \n",
-    "    ax.plot([-3.89578088] * 2,\n",
-    "            [ 1.19303364] * 2,\n",
-    "            [-100, 4.47697137598], \n",
-    "            color='red', alpha=1, linewidth=1.3, zorder=100, linestyle='dashed',\n",
-    "            label=r'minimum: $J(-3.90, 1.19) = 4.48$')\n",
-    "    ax.scatter([-3.89578088] * 2,\n",
-    "               [ 1.19303364] * 2,\n",
-    "               [-100, 4.47697137598], \n",
-    "               c='r', s=80, marker='x', alpha=1, linewidth=1.3, zorder=100, \n",
-    "               label=r'minimum: $J(-3.90, 1.19) = 4.48$')\n",
-    "    \n",
-    "    if show_gradient:\n",
-    "        ax.plot([3.0, 1.1],\n",
-    "                [3.0, 2.4],\n",
-    "                [263.0, 125.0], \n",
-    "                color='green', alpha=1, linewidth=1.3, zorder=100)\n",
-    "        ax.scatter([3.0],\n",
-    "                   [3.0],\n",
-    "                   [263.0], \n",
-    "                   c='g', s=30, marker='D', alpha=1, linewidth=1.3, zorder=100)\n",
-    "\n",
-    "    ax.margins(0,0,0)\n",
-    "    fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
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-    "def costplot2d(hypothesis, x, y, gradient_values=[], nohead=False):\n",
-    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
-    "    ax.set_xlabel(r'$\\theta_0$')\n",
-    "    ax.set_ylabel(r'$\\theta_1$')\n",
-    "    \n",
-    "    j = lambda theta0, theta1: costfun(hypothesis, x, y)([theta0, theta1])\n",
-    "    X = np.arange(-10, 10.1, 0.1)\n",
-    "    Y = np.arange(-1, 4.1, 0.1)\n",
-    "    X, Y = np.meshgrid(X, Y)\n",
-    "    Z = np.matrix([[J(hypothesis, [theta0, theta1], x, y) \n",
-    "                    for theta0, theta1 in zip(xRow, yRow)] \n",
-    "                   for xRow, yRow in zip(X, Y)])\n",
-    "    \n",
-    "    N = range(0, 800, 20)\n",
-    "    pl.contour(X, Y, Z, N, cmap='coolwarm', alpha=1)\n",
-    "\n",
-    "    ax.scatter([-3.89578088], [1.19303364], c='r', s=80, marker='x',\n",
-    "               label=r'minimum: $J(-3.90, 1.19) = 4.48$')\n",
-    "    \n",
-    "    if len(gradient_values) > 0:\n",
-    "        prev_theta = gradient_values[0][1]\n",
-    "        ax.scatter([prev_theta[0]], [prev_theta[1]],\n",
-    "                   c='g', s=30, marker='D', zorder=100)\n",
-    "        for cost, theta in gradient_values[1:]:\n",
-    "            dtheta = [theta[0] - prev_theta[0], theta[1] - prev_theta[1]]\n",
-    "            ax.arrow(prev_theta[0], prev_theta[1], dtheta[0], dtheta[1], \n",
-    "                     color='green', \n",
-    "                     head_width=(0.0 if nohead else 0.1), \n",
-    "                     head_length=(0.0 if nohead else 0.2),\n",
-    "                     zorder=100)\n",
-    "            prev_theta = theta\n",
-    "    \n",
-    "    return fig"
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-    "fig = costplot2d(h, x, y)\n",
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-    "### Cechy funkcji kosztu\n",
-    "* $J(\\theta)$ jest funkcją wypukłą\n",
-    "* $J(\\theta)$ posiada tylko jedno minimum lokalne"
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-    "## 1.5. Metoda gradientu prostego"
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-    "### Metoda gradientu prostego\n",
-    "Metoda znajdowania minimów lokalnych."
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-    "Idea:\n",
-    " * Zacznijmy od dowolnego $\\theta$.\n",
-    " * Zmieniajmy powoli $\\theta$ tak, aby zmniejszać $J(\\theta)$, aż w końcu znajdziemy minimum."
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-     "metadata": {},
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-   ],
-   "source": [
-    "costplot3d(h, x, y, show_gradient=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Przykładowe wartości kolejnych przybliżeń (sztuczne)\n",
-    "\n",
-    "gv = [[_, [3.0, 3.0]], [_, [2.6, 2.4]], [_, [2.2, 2.0]], [_, [1.6, 1.6]], [_, [0.4, 1.2]]]\n",
-    "\n",
-    "# Przygotowanie interaktywnego wykresu\n",
-    "\n",
-    "sliderSteps1 = widgets.IntSlider(min=0, max=3, step=1, value=0, description='kroki', width=300)\n",
-    "\n",
-    "def slide4(steps):\n",
-    "    costplot2d(h, x, y, gradient_values=gv[:steps+1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e9b6665115b748edb457981078f0042c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
-      "text/plain": [
-       "interactive(children=(IntSlider(value=0, description='kroki', max=3), Output()), _dom_classes=('widget-interact',))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.slide4>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "widgets.interact(slide4, steps=sliderSteps1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Metoda gradientu prostego\n",
-    "W każdym kroku będziemy aktualizować parametry $\\theta_j$:\n",
-    "\n",
-    "$$ \\theta_j := \\theta_j - \\alpha \\frac{\\partial}{\\partial \\theta_j} J(\\theta) \\quad \\mbox{ dla każdego } j $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "Współczynnik $\\alpha$ nazywamy *długością kroku* lub *współczynnikiem szybkości uczenia* (_learning rate_)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "$$ \\begin{array}{rcl}\n",
-    "\\dfrac{\\partial}{\\partial \\theta_j} J(\\theta)\n",
-    " & = & \\dfrac{\\partial}{\\partial \\theta_j} \\dfrac{1}{2m} \\displaystyle\\sum_{i = 1}^{m} \\left( h_{\\theta} \\left( x^{(i)} \\right) - y^{(i)} \\right) ^2 \\\\\n",
-    " & = & 2 \\cdot \\dfrac{1}{2m} \\displaystyle\\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) \\cdot \\dfrac{\\partial}{\\partial\\theta_j} \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) \\\\\n",
-    " & = & \\dfrac{1}{m}\\displaystyle\\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) \\cdot \\dfrac{\\partial}{\\partial\\theta_j} \\left( \\displaystyle\\sum_{i=0}^n \\theta_i x_i^{(i)} - y^{(i)} \\right)\\\\\n",
-    " & = & \\dfrac{1}{m}\\displaystyle\\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) -y^{(i)} \\right) x_j^{(i)} \\\\\n",
-    "\\end{array} $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "Czyli dla regresji liniowej jednej zmiennej:\n",
-    "\n",
-    "$$ h_\\theta(x) = \\theta_0 + \\theta_1x $$\n",
-    "\n",
-    "w każdym kroku będziemy aktualizować:\n",
-    "\n",
-    "$$\n",
-    "\\begin{array}{rcl}\n",
-    "\\theta_0 & := & \\theta_0 - \\alpha \\, \\dfrac{1}{m}\\displaystyle\\sum_{i=1}^m \\left( h_\\theta(x^{(i)})-y^{(i)} \\right) \\\\ \n",
-    "\\theta_1 & := & \\theta_1 - \\alpha \\, \\dfrac{1}{m}\\displaystyle\\sum_{i=1}^m \\left( h_\\theta(x^{(i)})-y^{(i)} \\right) x^{(i)}\\\\ \n",
-    "\\end{array}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "###### Uwaga!\n",
-    " * W każdym kroku aktualizujemy *jednocześnie* $\\theta_0$ i $\\theta_1$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    " * Kolejne kroki wykonujemy aż uzyskamy zbieżność"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Metoda gradientu prostego – implementacja"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Wyświetlanie macierzy w LaTeX-u\n",
-    "\n",
-    "def LatexMatrix(matrix):\n",
-    "    ltx = r'\\left[\\begin{array}'\n",
-    "    m, n = matrix.shape\n",
-    "    ltx += '{' + (\"r\" * n) + '}'\n",
-    "    for i in range(m):\n",
-    "        ltx += r\" & \".join([('%.4f' % j.item()) for j in matrix[i]]) + r\" \\\\ \"\n",
-    "    ltx += r'\\end{array}\\right]'\n",
-    "    return ltx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def gradient_descent(h, cost_fun, theta, x, y, alpha, eps):\n",
-    "    current_cost = cost_fun(h, theta, x, y)\n",
-    "    log = [[current_cost, theta]]  # log przechowuje wartości kosztu i parametrów\n",
-    "    m = len(y)\n",
-    "    while True:\n",
-    "        new_theta = [\n",
-    "            theta[0] - alpha/float(m) * sum(h(theta, x[i]) - y[i]\n",
-    "                                            for i in range(m)),   \n",
-    "            theta[1] - alpha/float(m) * sum((h(theta, x[i]) - y[i]) * x[i]\n",
-    "                                            for i in range(m))]\n",
-    "        theta = new_theta  # jednoczesna aktualizacja - używamy zmiennej tymaczasowej\n",
-    "        try:\n",
-    "            current_cost, prev_cost = cost_fun(h, theta, x, y), current_cost\n",
-    "        except OverflowError:\n",
-    "            break      \n",
-    "        if abs(prev_cost - current_cost) <= eps:\n",
-    "            break     \n",
-    "        log.append([current_cost, theta])\n",
-    "    return theta, log"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\large\\textrm{Wynik:}\\quad \\theta = \\left[\\begin{array}{r}-3.5074 \\\\ 1.1540 \\\\ \\end{array}\\right] \\quad J(\\theta) = 4.4908 \\quad \\textrm{po 644 iteracjach}$$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "best_theta, log = gradient_descent(h, J, [0.0, 0.0], x, y, alpha=0.02, eps=0.0001)\n",
-    "\n",
-    "display(Math(r'\\large\\textrm{Wynik:}\\quad \\theta = ' + \n",
-    "             LatexMatrix(np.matrix(best_theta).reshape(2,1)) + \n",
-    "            (r' \\quad J(\\theta) = %.4f' % log[-1][0])  \n",
-    "            + r' \\quad \\textrm{po %d iteracjach}' % len(log))) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Przygotowanie interaktywnego wykresu\n",
-    "\n",
-    "sliderSteps2 = widgets.IntSlider(min=0, max=500, step=1, value=1, description='kroki', width=300)\n",
-    "\n",
-    "def slide5(steps):\n",
-    "    costplot2d(h, x, y, gradient_values=log[:steps+1], nohead=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fc6dd6fa531b4d8eac47c456831f61a4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
-      "text/plain": [
-       "interactive(children=(IntSlider(value=1, description='kroki', max=500), Button(description='Run Interact', style=ButtonStyle()), Output()), _dom_classes=('widget-interact',))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.slide5>"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "widgets.interact_manual(slide5, steps=sliderSteps2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Współczynnik $\\alpha$ (długość kroku)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    " * Jeżeli długość kroku jest zbyt mała, algorytm może działać zbyt wolno."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    " * Jeżeli długość kroku jest zbyt duża, algorytm może nie być zbieżny."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## 1.6. Regresja liniowa wielu zmiennych"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Przykład – ceny mieszkań"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "y : price    x1: isNew    x2: rooms    x3: floor    x4: location x5: sqrMetres\n",
-      "476118.0     False        3            1            Centrum      78          \n",
-      "459531.0     False        3            2            Sołacz       62          \n",
-      "411557.0     False        3            0            Sołacz       15          \n",
-      "496416.0     False        4            0            Sołacz       14          \n",
-      "406032.0     False        3            0            Sołacz       15          \n",
-      "450026.0     False        3            1            Naramowice   80          \n",
-      "571229.15    False        2            4            Wilda        39          \n",
-      "325000.0     False        3            1            Grunwald     54          \n",
-      "268229.0     False        2            1            Grunwald     90          \n"
-     ]
-    }
-   ],
-   "source": [
-    "reader = csv.reader(open('data02.tsv'), delimiter='\\t')\n",
-    "for i, row in enumerate(list(reader)[:10]):\n",
-    "    if i == 0:\n",
-    "        print(' '.join(['{}: {:8}'.format('x' + str(j) if j > 0 else 'y ', entry)\n",
-    "                        for j, entry in enumerate(row)]))\n",
-    "    else:\n",
-    "        print(' '.join(['{:12}'.format(entry) for entry in row]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "$$ x^{(2)} = ({\\rm \"False\"}, 3, 2, {\\rm \"Sołacz\"}, 62), \\quad x_3^{(2)} = 2 $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Hipoteza"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "W naszym przypadku:\n",
-    "\n",
-    "$$ h_\\theta(x) = \\theta_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\theta_3 x_3 + \\theta_4 x_4 + \\theta_5 x_5 $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "W ogólności:\n",
-    "\n",
-    "$$ h_\\theta(x) = \\theta_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\ldots + \\theta_n x_n $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "Jeżeli zdefiniujemy $x_0 = 1$:\n",
-    "\n",
-    "$$\n",
-    "\\begin{array}{rcl}\n",
-    "h_\\theta(x)\n",
-    " & = & \\theta_0 x_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\ldots + \\theta_n x_n \\\\\n",
-    " & = & \\displaystyle\\sum_{i=0}^{n} \\theta_i x_i \\\\\n",
-    " & = & \\theta^T \\, x \\\\\n",
-    " & = & x^T \\, \\theta \\\\\n",
-    "\\end{array}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Metoda gradientu prostego – notacja macierzowa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "$$\n",
-    "X=\\left[\\begin{array}{cc}\n",
-    "1 & \\left( \\vec x^{(1)} \\right)^T \\\\\n",
-    "1 & \\left( \\vec x^{(2)} \\right)^T \\\\\n",
-    "\\vdots & \\vdots\\\\\n",
-    "1 & \\left( \\vec x^{(m)} \\right)^T \\\\\n",
-    "\\end{array}\\right] \n",
-    "= \\left[\\begin{array}{cccc}\n",
-    "1 & x_1^{(1)} & \\cdots & x_n^{(1)} \\\\\n",
-    "1 & x_1^{(2)} & \\cdots & x_n^{(2)} \\\\\n",
-    "\\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
-    "1 & x_1^{(m)} & \\cdots & x_n^{(m)} \\\\\n",
-    "\\end{array}\\right]\n",
-    "\\quad\n",
-    "\\vec{y} = \n",
-    "\\left[\\begin{array}{c}\n",
-    "y^{(1)}\\\\\n",
-    "y^{(2)}\\\\\n",
-    "\\vdots\\\\\n",
-    "y^{(m)}\\\\\n",
-    "\\end{array}\\right]\n",
-    "\\quad\n",
-    "\\theta = \\left[\\begin{array}{c}\n",
-    "\\theta_0\\\\\n",
-    "\\theta_1\\\\\n",
-    "\\vdots\\\\\n",
-    "\\theta_n\\\\\n",
-    "\\end{array}\\right]\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Wczytwanie danych z pliku za pomocą numpy\n",
-    "# Wersje macierzowe funkcji rysowania wykresów punktowych oraz krzywej regresyjnej\n",
-    "\n",
-    "data = np.loadtxt('data01.csv', delimiter=',')\n",
-    "m, n_plus_1 = data.shape\n",
-    "n = n_plus_1 - 1\n",
-    "Xn = data[:, 0:n].reshape(m, n)\n",
-    "\n",
-    "# Dodaj kolumnę jedynek do macierzy\n",
-    "XMx = np.matrix(np.concatenate((np.ones((m, 1)), Xn), axis=1)).reshape(m, n_plus_1)\n",
-    "yMx = np.matrix(data[:, 1]).reshape(m, 1)\n",
-    "\n",
-    "def hMx(theta, X):\n",
-    "    return X * theta\n",
-    "\n",
-    "def regdotsMx(X, y):    \n",
-    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
-    "    ax.scatter([X[:, 1]], [y], c='r', s=50, label='Dane')\n",
-    "    \n",
-    "    ax.set_xlabel('Populacja')\n",
-    "    ax.set_ylabel('Zysk')\n",
-    "    ax.margins(.05, .05)\n",
-    "    pl.ylim(y.min() - 1, y.max() + 1)\n",
-    "    pl.xlim(np.min(X[:, 1]) - 1, np.max(X[:, 1]) + 1)\n",
-    "    return fig\n",
-    "\n",
-    "def reglineMx(fig, fun, theta, X):\n",
-    "    ax = fig.axes[0]\n",
-    "    x0, x1 = np.min(X[:, 1]), np.max(X[:, 1])\n",
-    "    L = [x0, x1]\n",
-    "    LX = np.matrix([1, x0, 1, x1]).reshape(2, 2)\n",
-    "    ax.plot(L, fun(theta, LX), linewidth='2',\n",
-    "            label=(r'$y={theta0:.2}{op}{theta1:.2}x$'.format(\n",
-    "                theta0=float(theta[0][0]),\n",
-    "                theta1=(float(theta[1][0]) if theta[1][0] >= 0 else float(-theta[1][0])),\n",
-    "                op='+' if theta[1][0] >= 0 else '-')))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Funkcja kosztu – notacja macierzowa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "$$J(\\theta)=\\dfrac{1}{2|\\vec y|}\\left(X\\theta-\\vec{y}\\right)^T\\left(X\\theta-\\vec{y}\\right)$$ \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\Large J(\\theta) = 4.5885$$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Wersja macierzowa funkcji kosztu\n",
-    "\n",
-    "def JMx(theta,X,y):\n",
-    "    m = len(y)\n",
-    "    J = 1.0 / (2.0 * m) * ((X * theta - y) . T * ( X * theta - y))\n",
-    "    return J.item()\n",
-    "\n",
-    "thetaMx = np.matrix([-5, 1.3]).reshape(2, 1) \n",
-    "\n",
-    "cost = JMx(thetaMx,XMx,yMx) \n",
-    "display(Math(r'\\Large J(\\theta) = %.4f' % cost))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Gradient – notacja macierzowa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "$$\\nabla J(\\theta) = \\frac{1}{|\\vec y|} X^T\\left(X\\theta-\\vec y\\right)$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\large \\theta = \\left[\\begin{array}{r}-5.0000 \\\\ 1.3000 \\\\ \\end{array}\\right]\\quad\\large \\nabla J(\\theta) = \\left[\\begin{array}{r}-0.2314 \\\\ -0.3027 \\\\ \\end{array}\\right]$$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Wersja macierzowa gradientu funkcji kosztu\n",
-    "\n",
-    "def dJMx(theta,X,y):\n",
-    "    return 1.0 / len(y) * (X.T * (X * theta - y)) \n",
-    "\n",
-    "thetaMx2 = np.matrix([-5, 1.3]).reshape(2, 1)\n",
-    "\n",
-    "display(Math(r'\\large \\theta = ' + LatexMatrix(thetaMx2) + \n",
-    "             r'\\quad' + r'\\large \\nabla J(\\theta) = ' \n",
-    "             + LatexMatrix(dJMx(thetaMx2,XMx,yMx))))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Algorytm gradientu prostego – notacja macierzowa"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "$$ \\theta := \\theta - \\alpha \\, \\nabla J(\\theta) $$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "notes"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\large\\textrm{Wynik:}\\quad \\theta = \\left[\\begin{array}{r}-3.7217 \\\\ 1.1755 \\\\ \\end{array}\\right] \\quad J(\\theta) = 4.4797 \\quad \\textrm{po 1734 iteracjach}$$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Implementacja algorytmu gradientu prostego za pomocą numpy i macierzy\n",
-    "\n",
-    "def GDMx(fJ, fdJ, theta, X, y, alpha=0.1, eps=10**-3):\n",
-    "    current_cost = fJ(theta, X, y)\n",
-    "    log = [[current_cost, theta]]\n",
-    "    while True:\n",
-    "        theta = theta - alpha * fdJ(theta, X, y) # implementacja wzoru\n",
-    "        current_cost, prev_cost = fJ(theta, X, y), current_cost\n",
-    "        if abs(prev_cost - current_cost) <= eps:\n",
-    "            break\n",
-    "        log.append([current_cost, theta]) \n",
-    "    return theta, log\n",
-    "\n",
-    "thetaStartMx = np.matrix([0, 0]).reshape(2, 1)\n",
-    "\n",
-    "# Zmieniamy wartości alpha (rozmiar kroku) oraz eps (kryterium stopu)\n",
-    "thetaBestMx, log = GDMx(JMx, dJMx, thetaStartMx, \n",
-    "                        XMx, yMx, alpha=0.01, eps=0.00001)\n",
-    "\n",
-    "######################################################################\n",
-    "display(Math(r'\\large\\textrm{Wynik:}\\quad \\theta = ' + \n",
-    "             LatexMatrix(thetaBestMx) + \n",
-    "             (r' \\quad J(\\theta) = %.4f' % log[-1][0])  \n",
-    "             + r' \\quad \\textrm{po %d iteracjach}' % len(log)))  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## 1.7. Metoda gradientu prostego w praktyce"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "### Kryterium stopu"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "Na wykresie zobaczymy porównanie regresji dla różnych wartości `eps`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
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-    "fig = regdotsMx(XMx, yMx)\n",
-    "theta_e1, log1 = GDMx(JMx, dJMx, thetaStartMx, XMx, yMx, alpha=0.01, eps=0.01)\n",
-    "reglineMx(fig, hMx, theta_e1, XMx)\n",
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-      "text/latex": [
-       "$$\\theta_{10^{-2}} = \\left[\\begin{array}{r}0.0511 \\\\ 0.7957 \\\\ \\end{array}\\right]\\quad\\theta_{10^{-6}} = \\left[\\begin{array}{r}-3.8407 \\\\ 1.1875 \\\\ \\end{array}\\right]$$"
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-       "<IPython.core.display.Math object>"
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-    "display(Math(r'\\theta_{10^{-2}} = ' + LatexMatrix(theta_e1) +\n",
-    "             r'\\quad\\theta_{10^{-6}} = ' + LatexMatrix(theta_e2)))"
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-   "cell_type": "markdown",
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-   },
-   "source": [
-    "### Długość kroku ($\\alpha$)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": true,
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-     "slide_type": "notes"
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-   "outputs": [],
-   "source": [
-    "# Jak zmienia się koszt w kolejnych krokach w zależności od alfa\n",
-    "\n",
-    "def costchangeplot(logs):\n",
-    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
-    "    ax.set_xlabel('krok')\n",
-    "    ax.set_ylabel(r'$J(\\theta)$')\n",
-    "\n",
-    "    X = np.arange(0, 500, 1)\n",
-    "    Y = [logs[step][0] for step in X]\n",
-    "    ax.plot(X, Y, linewidth='2', label=(r'$J(\\theta)$'))\n",
-    "    return fig\n",
-    "\n",
-    "def slide7(alpha):\n",
-    "    best_theta, log = gradient_descent(h, J, [0.0, 0.0], x, y, alpha=alpha, eps=0.0001)\n",
-    "    fig = costchangeplot(log)\n",
-    "    legend(fig)\n",
-    "\n",
-    "sliderAlpha1 = widgets.FloatSlider(min=0.01, max=0.03, step=0.001, value=0.02, description=r'$\\alpha$', width=300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
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-    "slideshow": {
-     "slide_type": "subslide"
-    }
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-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f617b06eebc49168e4b8daac54528cd",
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-       "version_minor": 0
-      },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another notebook frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.02, description='$\\\\alpha$', max=0.03, min=0.01, step=0.001), Button(description='Run Interact', style=ButtonStyle()), Output()), _dom_classes=('widget-interact',))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.slide7>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "widgets.interact_manual(slide7, alpha=sliderAlpha1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## 1.8 Regresja liniowa – dodatek\n",
-    "### Regresja liniowa za pomocą macierzy normalnej"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
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-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "Zamiast korzystać z algorytmu gradientu prostego\n",
-    "możemy bezpośrednio obliczyć minimum $J(\\theta)$ dla regresji liniowej ze wzoru: \n",
-    "\n",
-    "$$ \\theta = \\left( X^T X \\right)^{-1} \\, X^T \\, \\vec y $$"
-   ]
-  },
-  {
-   "cell_type": "code",
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-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  },
-  "livereveal": {
-   "start_slideshow_at": "selected",
-   "theme": "amu"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/w1/zumz181b.pdf b/w1/zumz181b.pdf
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diff --git a/wyk/1_Wprowadzenie.ipynb b/wyk/1_Wprowadzenie.ipynb
new file mode 100644
index 0000000..7524151
--- /dev/null
+++ b/wyk/1_Wprowadzenie.ipynb
@@ -0,0 +1,649 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Uczenie maszynowe\n",
+    "# 1. Wprowadzenie"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 1.1. Czym jest uczenie maszynowe?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" src=\"terms-cloud.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Sztuczna inteligencja (*artificial intelligence*)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Naśladowanie ludzkich procesów poznawczych za pomocą komputerów"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Konstruowanie systemów (maszyn), których działanie podobne jest do przejawów ludzkiej inteligencji"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Dziedzina nauki, która zajmuje się naśladowaniem ludzkiej inteligencji przez komputery"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Obejmuje m.in. logikę rozmytą, algorytmy ewolucyjne, robotykę i uczenie maszynowe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" src=\"venn-ai.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Uczenie maszynowe (*machine learning*)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Tworzenie systemów, które potrafią doskonalić się przy pomocy zgromadzonego doświadczenia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" src=\"venn-ai-ml.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Sieci neuronowe (neural networks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Rodzaj struktur matematycznych, które wykonują obliczenia przy pomocy elementów zwanych _sztucznymi neuronami_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Budowa sieci neuronowych i zasady działania sztucznych neuronów były luźno inspirowane działaniem neuronów w mózgu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" src=\"venn-ai-ml-nn.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Uczenie głębokie (deep learning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Użycie sieci neuronowych do automatycznego wydobywania cech z surowych danych"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" src=\"venn-ai-ml-nn-dl.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Data science"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* Dziedzina nauki zajmująca się przetwarzaniem danych w celu wydobycia z nich wiedzy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" src=\"venn-ai-ml-nn-dl-ds.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Uczenie maszynowe – klasyczne definicje"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"float: right;\" src=\"https://upload.wikimedia.org/wikipedia/commons/f/f8/This_is_the_photo_of_Arthur_Samuel.jpg\"/>\n",
+    "\n",
+    "> Uczenie maszynowe to dziedzina nauki,\n",
+    "> która daje komputerom umiejętność uczenia się\n",
+    "> bez programowania ich _explicite_.\n",
+    "\n",
+    "> &mdash; <cite>Arthur Samuel, 1959</cite>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"float: right;\" width=\"20%\" src=\"http://mediad.publicbroadcasting.net/p/wamc/files/styles/x_large/public/201401/tom_mitchell.jpg\"/>\n",
+    "\n",
+    "> Mówimy, że program komputerowy **uczy się**\n",
+    "> z doświadczenia E w odniesieniu do zadania T i miary skuteczności P,\n",
+    "> jeżeli jego skuteczność wykonywania zadania T mierzona według P\n",
+    "> wzrasta z doświadczeniem E.\n",
+    "\n",
+    "> &mdash; <cite>Tom Mitchell, 1998</cite>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" width=\"40%\" src=\"https://static1.squarespace.com/static/5150aec6e4b0e340ec52710a/t/51525c33e4b0b3e0d10f77ab/1364352052403/Data_Science_VD.png\"/>\n",
+    "\n",
+    "<sub>Źródło: Drew Conway, http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram</sub>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Uczenie maszynowe to:\n",
+    "\n",
+    "* doskonalenie działania dla pewnych zadań na podstawie doświadczenia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* tworzenie systemów, które doskonalą swoje działania na podstawie przeszłych doświadczeń"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "* zestaw metod, które potrafią w sposób automatyczny wykrywać wzorce w danych, a następnie używać wcześniej niezaobserwowanych wzorców do przewidywania przyszłych zjawisk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Charakterystyczne cechy uczenia maszynowego:\n",
+    "\n",
+    "* „automatyzacja automatyzacji”\n",
+    "* komputer „sam się programuje”\n",
+    "* modelowanie danych zastępuje pisanie programu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" width=\"80%\" src=\"https://recast.ai/blog/wp-content/uploads/2017/02/image20.png\"/>\n",
+    "\n",
+    "<sub>Źródło: https://recast.ai/blog/machine-learning-algorithms/</sub>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import IPython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"800\"\n",
+       "            height=\"600\"\n",
+       "            src=\"https://www.youtube.com/embed/R9OHn5ZF4Uo\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x13c6c739370>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "IPython.display.YouTubeVideo('R9OHn5ZF4Uo', width=800, height=600)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 1.2. Zastosowania uczenia maszynowego"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* rozpoznawanie i rozumienie mowy\n",
+    "* rozpoznawanie obrazów\n",
+    "* tłumaczenie maszynowe\n",
+    "* systemy rekomendacyjne\n",
+    "* detekcja spamu\n",
+    "* klasyfikacja dokumentów/obrazów\n",
+    "* analiza nastrojów\n",
+    "* rozpoznawanie pisma odręcznego\n",
+    "* samochody autonomiczne\n",
+    "* przewidywanie kursów giełdowych\n",
+    "* automatyczna diagnostyka medyczna\n",
+    "* analiza genów\n",
+    "* sztuczna inteligencja w grach"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Co potrafi uczenie maszynowe?\n",
+    "\n",
+    "* *Two Minute Papers* - streszczenia ciekawszych artykułów naukowych z dziedziny ML: https://www.youtube.com/user/keeroyz, np.:\n",
+    "  * AI gra w chowanego i \"psuje\" grę: https://youtu.be/Lu56xVlZ40M\n",
+    "* Generowanie twarzy itp.:\n",
+    "  * https://thispersondoesnotexist.com\n",
+    "  * https://thiscatdoesnotexist.com\n",
+    "* Blog inicjatywy OpenAI: https://openai.com/blog, np.:\n",
+    "  * generowanie tekstu: https://openai.com/blog/better-language-models\n",
+    "  * generowanie obrazów na podstawie opisu słownego: https://openai.com/blog/dall-e\n",
+    "* Zamiana rysunków odręcznych na zdjęcia: https://affinelayer.com/pixsrv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 1.3. Metody uczenia maszynowego"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Z jakimi rodzajami zadań mamy do czynienia?\n",
+    "\n",
+    "* Uczenie nadzorowane\n",
+    "  * Regresja\n",
+    "  * Klasyfikacja\n",
+    "* Uczenie nienadzorowane\n",
+    "  * Klastrowanie\n",
+    "* Uczenie przez wzmacnianie\n",
+    "* Systemy rekomendacyjne"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Klasyfikator\n",
+    "\n",
+    "* Klasyfikator to funkcja $h$, która przykładowi $x$ przyporządkowuje prognozowaną wartość $h(x)$.\n",
+    "* Jeżeli funkcja $h$ jest ciągła, to mówimy o zagadnieniu **regresji**.\n",
+    "* Jeżeli funkcja $h$ jest dyskretna, to mówimy o zagadnieniu **klasyfikacji**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Algorytm uczący\n",
+    "\n",
+    "* Dane są przykładowe obserwacje $(X, y)$.\n",
+    "* Staramy się dobrać funkcję (klasyfikator) $h$ tak, żeby $h(x) \\sim y$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "W jaki sposób można określić, czy klasyfikator jest „dobry”?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Podział metod uczenia maszynowego\n",
+    "\n",
+    "> \\[Każdy algorytm uczenia maszynowego\\] stanowi kombinację dokładnie trzech składników.\n",
+    "> Te składniki to:\n",
+    "> * reprezentacja\n",
+    "> * ewaluacja\n",
+    "> * optymalizacja\n",
+    "\n",
+    "> &mdash; Pedro Domingos, “A Few Useful Things to Know about Machine Learning”"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Reprezentacja\n",
+    "\n",
+    "* drzewa decyzyjne\n",
+    "* regresja liniowa\n",
+    "* regresja logistyczna\n",
+    "* naiwny klasyfikator bayesowski\n",
+    "* algorytm $k$ najbliższych sąsiadów\n",
+    "* sieci neuronowe\n",
+    "* maszyny wektorów nośnych\n",
+    "* algorytmy genetyczne\n",
+    "* ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Ewaluacja\n",
+    "\n",
+    "* skuteczność (dokładność)\n",
+    "* precyzja i pokrycie\n",
+    "* błąd średniokwadratowy\n",
+    "* _information gain_\n",
+    "* _logistic loss_\n",
+    "* BLEU\n",
+    "* ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Optymalizacja\n",
+    "\n",
+    "* optymalizacja kombinatoryczna:\n",
+    "  * wyszukiwanie zachłanne,\n",
+    "  * _beam search_...\n",
+    "* optymalizacja ciągła:\n",
+    "  * nieograniczona:\n",
+    "    * metoda gradientu prostego,\n",
+    "    * metoda Newtona...\n",
+    "  * ograniczona:\n",
+    "    * programowanie liniowe..."
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  },
+  "livereveal": {
+   "start_slideshow_at": "selected",
+   "theme": "white"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/wyk/2_Regresja_liniowa.ipynb b/wyk/2_Regresja_liniowa.ipynb
new file mode 100644
index 0000000..8cfcfcf
--- /dev/null
+++ b/wyk/2_Regresja_liniowa.ipynb
@@ -0,0 +1,48213 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Uczenie maszynowe\n",
+    "# 2. Regresja liniowa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 2.1. Funkcja kosztu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Zadanie\n",
+    "Znając $x$ – ludność miasta (w dziesiątkach tysięcy mieszkańców),\n",
+    "należy przewidzieć $y$ – dochód firmy transportowej (w dziesiątkach tysięcy dolarów).\n",
+    "\n",
+    "(Dane pochodzą z kursu „Machine Learning”, Andrew Ng, Coursera)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as pl\n",
+    "import ipywidgets as widgets\n",
+    "\n",
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "\n",
+    "from IPython.display import display, Math, Latex"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Dane"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.1101,17.592\n",
+      "\n",
+      "5.5277,9.1302\n",
+      "\n",
+      "8.5186,13.662\n",
+      "\n",
+      "7.0032,11.854\n",
+      "\n",
+      "5.8598,6.8233\n",
+      "\n",
+      "8.3829,11.886\n",
+      "\n",
+      "7.4764,4.3483\n",
+      "\n",
+      "8.5781,12\n",
+      "\n",
+      "6.4862,6.5987\n",
+      "\n",
+      "5.0546,3.8166\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('data01_train.csv') as data:\n",
+    "    for line in data.readlines()[:10]:\n",
+    "        print(line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Wczytanie danych"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = [6.1101, 5.5277, 8.5186, 7.0032, 5.8598, 8.3829, 7.4764, 8.5781, 6.4862, 5.0546]\n",
+      "y = [17.592, 9.1302, 13.662, 11.854, 6.8233, 11.886, 4.3483, 12.0, 6.5987, 3.8166]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import csv\n",
+    "\n",
+    "reader = csv.reader(open('data01_train.csv'), delimiter=',')\n",
+    "\n",
+    "x = list()\n",
+    "y = list()\n",
+    "for xi, yi in reader:\n",
+    "    x.append(float(xi))\n",
+    "    y.append(float(yi)) \n",
+    "    \n",
+    "print('x = {}'.format(x[:10])) \n",
+    "print('y = {}'.format(y[:10]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Hipoteza i parametry modelu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Jak przewidzieć $y$ na podstawie danego $x$? W celu odpowiedzi na to pytanie będziemy starać się znaleźć taką funkcję $h(x)$, która będzie najlepiej obrazować zależność między $x$ a $y$, tj. $y \\sim h(x)$.\n",
+    "\n",
+    "Zacznijmy od najprostszego przypadku, kiedy $h(x)$ jest po prostu funkcją liniową. Ogólny wzór funkcji liniowej to"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$ h(x) = a \\, x + b $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Pamiętajmy jednak, że współczynniki $a$ i $b$ nie są w tej chwili dane z góry – naszym zadaniem właśnie będzie znalezienie takich ich wartości, żeby $h(x)$ było „możliwie jak najbliżej” $y$ (co właściwie oznacza to sformułowanie, wyjaśnię potem)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Poszukiwaną funkcję $h$ będziemy nazywać **funkcją hipotezy**, a jej współczynniki – **parametrami modelu**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "W teorii uczenia maszynowego parametry modelu oznacza się na ogół grecką literą $\\theta$ z odpowiednimi indeksami, dlatego powyższy wzór opisujący liniową funkcję hipotezy zapiszemy jako\n",
+    "$$ h(x) = \\theta_0 + \\theta_1 x $$\n",
+    "\n",
+    "**Parametry modelu** tworzą wektor, który oznaczymy po prostu przez $\\theta$:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "$$ \\theta = \\left[\\begin{array}{c}\\theta_0\\\\ \\theta_1\\end{array}\\right] $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Żeby podkreślić fakt, że funkcja hipotezy zależy od parametrów modelu, będziemy pisać $h_\\theta$ zamiast $h$:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$ h_{\\theta}(x) = \\theta_0 + \\theta_1 x $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Przyjrzyjmy się teraz, jak wyglądają dane, które mamy modelować:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Na poniższym wykresie możesz spróbować ręcznie dopasować parametry modelu $\\theta_0$ i $\\theta_1$ tak, aby jak najlepiej modelowały zależność między $x$ a $y$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Funkcje rysujące wykres kropkowy oraz prostą regresyjną\n",
+    "\n",
+    "def regdots(x, y):    \n",
+    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
+    "    ax.scatter(x, y, c='r', s=50, label='Dane')\n",
+    "    \n",
+    "    ax.set_xlabel(u'Wielkość miejscowości [dzies. tys. mieszk.]')\n",
+    "    ax.set_ylabel(u'Dochód firmy [dzies. tys. dolarów]')\n",
+    "    ax.margins(.05, .05)\n",
+    "    pl.ylim(min(y) - 1, max(y) + 1)\n",
+    "    pl.xlim(min(x) - 1, max(x) + 1)\n",
+    "    return fig\n",
+    "\n",
+    "def regline(fig, fun, theta, x):\n",
+    "    ax = fig.axes[0]\n",
+    "    x0, x1 = min(x), max(x)\n",
+    "    X = [x0, x1]\n",
+    "    Y = [fun(theta, x) for x in X]\n",
+    "    ax.plot(X, Y, linewidth='2',\n",
+    "            label=(r'$y={theta0}{op}{theta1}x$'.format(\n",
+    "                theta0=theta[0],\n",
+    "                theta1=(theta[1] if theta[1] >= 0 else -theta[1]),\n",
+    "                op='+' if theta[1] >= 0 else '-')))\n",
+    "\n",
+    "def legend(fig):\n",
+    "    ax = fig.axes[0]\n",
+    "    handles, labels = ax.get_legend_handles_labels()\n",
+    "    # try-except block is a fix for a bug in Poly3DCollection\n",
+    "    try:\n",
+    "        fig.legend(handles, labels, fontsize='15', loc='lower right')\n",
+    "    except AttributeError:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
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+      "text/plain": [
+       "<Figure size 691.2x388.8 with 1 Axes>"
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+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = regdots(x,y)\n",
+    "legend(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Hipoteza: funkcja liniowa jednej zmiennej\n",
+    "\n",
+    "def h(theta, x):\n",
+    "    return theta[0] + theta[1] * x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Przygotowanie interaktywnego wykresu\n",
+    "\n",
+    "sliderTheta01 = widgets.FloatSlider(min=-10, max=10, step=0.1, value=0, description=r'$\\theta_0$', width=300)\n",
+    "sliderTheta11 = widgets.FloatSlider(min=-5, max=5, step=0.1, value=0, description=r'$\\theta_1$', width=300)\n",
+    "\n",
+    "def slide1(theta0, theta1):\n",
+    "    fig = regdots(x, y)\n",
+    "    regline(fig, h, [theta0, theta1], x)\n",
+    "    legend(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7d796d13ab5455d96386defc018b025",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='$\\\\theta_0$', max=10.0, min=-10.0), FloatSlider(valu…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.slide1(theta0, theta1)>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "widgets.interact_manual(slide1, theta0=sliderTheta01, theta1=sliderTheta11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Skąd wiadomo, że przewidywania modelu (wartości funkcji $h(x)$) zgadzaja się z obserwacjami (wartości $y$)?\n",
+    "\n",
+    "Aby to zmierzyć wprowadzimy pojęcie funkcji kosztu."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Funkcja kosztu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Funkcję kosztu zdefiniujemy w taki sposób, żeby odzwierciedlała ona różnicę między przewidywaniami modelu a obserwacjami.\n",
+    "\n",
+    "Jedną z możliwosci jest zdefiniowanie funkcji kosztu jako wartość **błędu średniokwadratowego** (metoda najmniejszych kwadratów, *root-mean-square error, RMSE*):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$ J(\\theta) \\, = \\, \\frac{1}{2m} \\sum_{i = 1}^{m} \\left( h_{\\theta} \\left( x^{(i)} \\right) - y^{(i)} \\right) ^2 $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "gdzie $m$ jest liczbą wszystkich przykładów (obserwacji), czyli wielkością zbioru danych uczących.\n",
+    "\n",
+    "W powyższym wzorze sumujemy kwadraty różnic między przewidywaniami modelu ($h_\\theta \\left( x^{(i)} \\right)$) a obserwacjami ($y^{(i)}$) po wszystkich przykładach $i$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Teraz nasze zadanie sprowadza się do tego, że będziemy szukać takich parametrów $\\theta = \\left[\\begin{array}{c}\\theta_0\\\\ \\theta_1\\end{array}\\right]$, które minimalizują fukcję kosztu $J(\\theta)$:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$ \\hat\\theta = \\mathop{\\arg\\min}_{\\theta} J(\\theta) $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$ \\theta \\in \\mathbb{R}^2, \\quad J \\colon \\mathbb{R}^2 \\to \\mathbb{R} $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Proszę zwrócić uwagę, że dziedziną funkcji kosztu jest zbiór wszystkich możliwych wartości parametrów $\\theta$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "$$ J(\\theta_0, \\theta_1) \\, = \\, \\frac{1}{2m} \\sum_{i = 1}^{m} \\left( \\theta_0 + \\theta_1 x^{(i)} - y^{(i)} \\right) ^2 $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def J(h, theta, x, y):\n",
+    "    \"\"\"Funkcja kosztu\"\"\"\n",
+    "    m = len(y)\n",
+    "    return 1.0 / (2 * m) * sum((h(theta, x[i]) - y[i])**2 for i in range(m))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Oblicz wartość funkcji kosztu i pokaż na wykresie\n",
+    "\n",
+    "def regline2(fig, fun, theta, xx, yy):\n",
+    "    \"\"\"Rysuj regresję liniową\"\"\"\n",
+    "    ax = fig.axes[0]\n",
+    "    x0, x1 = min(xx), max(xx)\n",
+    "    X = [x0, x1]\n",
+    "    Y = [fun(theta, x) for x in X]\n",
+    "    cost = J(fun, theta, xx, yy)\n",
+    "    ax.plot(X, Y, linewidth='2',\n",
+    "            label=(r'$y={theta0}{op}{theta1}x, \\; J(\\theta)={cost:.3}$'.format(\n",
+    "                theta0=theta[0],\n",
+    "                theta1=(theta[1] if theta[1] >= 0 else -theta[1]),\n",
+    "                op='+' if theta[1] >= 0 else '-',\n",
+    "                cost=cost)))\n",
+    "\n",
+    "sliderTheta02 = widgets.FloatSlider(min=-10, max=10, step=0.1, value=0, description=r'$\\theta_0$', width=300)\n",
+    "sliderTheta12 = widgets.FloatSlider(min=-5, max=5, step=0.1, value=0, description=r'$\\theta_1$', width=300)\n",
+    "\n",
+    "def slide2(theta0, theta1):\n",
+    "    fig = regdots(x, y)\n",
+    "    regline2(fig, h, [theta0, theta1], x, y)\n",
+    "    legend(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Poniższy interaktywny wykres pokazuje wartość funkcji kosztu $J(\\theta)$. Czy teraz łatwiej jest dobrać parametry modelu?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b272821647e462f8a8cd6304bcbdd97",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='$\\\\theta_0$', max=10.0, min=-10.0), FloatSlider(valu…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.slide2(theta0, theta1)>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "widgets.interact_manual(slide2, theta0=sliderTheta02, theta1=sliderTheta12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Funkcja kosztu jako funkcja zmiennej $\\theta$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Funkcja kosztu zdefiniowana jako RMSE jest funkcją zmiennej wektorowej $\\theta$, czyli funkcją dwóch zmiennych rzeczywistych: $\\theta_0$ i $\\theta_1$.\n",
+    "    \n",
+    "Zobaczmy, jak wygląda jej wykres."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Wykres funkcji kosztu dla ustalonego theta_1=1.0\n",
+    "\n",
+    "def costfun(fun, x, y):\n",
+    "    return lambda theta: J(fun, theta, x, y)\n",
+    "\n",
+    "def costplot(hypothesis, x, y, theta1=1.0):\n",
+    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
+    "    ax.set_xlabel(r'$\\theta_0$')\n",
+    "    ax.set_ylabel(r'$J(\\theta)$')\n",
+    "    j = costfun(hypothesis, x, y)\n",
+    "    fun = lambda theta0: j([theta0, theta1])\n",
+    "    X = np.arange(-10, 10, 0.1)\n",
+    "    Y = [fun(x) for x in X]\n",
+    "    ax.plot(X, Y, linewidth='2', label=(r'$J(\\theta_0, {theta1})$'.format(theta1=theta1)))\n",
+    "    return fig\n",
+    "\n",
+    "def slide3(theta1):\n",
+    "    fig = costplot(h, x, y, theta1)\n",
+    "    legend(fig)\n",
+    "\n",
+    "sliderTheta13 = widgets.FloatSlider(min=-5, max=5, step=0.1, value=1.0, description=r'$\\theta_1$', width=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "399408d2a4ef42b88c80685689b4ce98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=1.0, description='$\\\\theta_1$', max=5.0, min=-5.0), Button(description…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.slide3(theta1)>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "widgets.interact_manual(slide3, theta1=sliderTheta13)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Wykres funkcji kosztu względem theta_0 i theta_1\n",
+    "\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "import pylab\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "def costplot3d(hypothesis, x, y, show_gradient=False):\n",
+    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
+    "    ax = fig.add_subplot(111, projection='3d')\n",
+    "    fig.subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0)\n",
+    "    ax.set_xlabel(r'$\\theta_0$')\n",
+    "    ax.set_ylabel(r'$\\theta_1$')\n",
+    "    ax.set_zlabel(r'$J(\\theta)$')\n",
+    "    \n",
+    "    j = lambda theta0, theta1: costfun(hypothesis, x, y)([theta0, theta1])\n",
+    "    X = np.arange(-10, 10.1, 0.1)\n",
+    "    Y = np.arange(-1, 4.1, 0.1)\n",
+    "    X, Y = np.meshgrid(X, Y)\n",
+    "    Z = np.array([[J(hypothesis, [theta0, theta1], x, y) \n",
+    "                    for theta0, theta1 in zip(xRow, yRow)] \n",
+    "                   for xRow, yRow in zip(X, Y)])\n",
+    "    \n",
+    "    ax.plot_surface(X, Y, Z, rstride=2, cstride=8, linewidth=0.5,\n",
+    "                    alpha=0.5, cmap='jet', zorder=0,\n",
+    "                    label=r\"$J(\\theta)$\")\n",
+    "    ax.view_init(elev=20., azim=-150)\n",
+    "\n",
+    "    ax.set_xlim3d(-10, 10);\n",
+    "    ax.set_ylim3d(-1, 4);\n",
+    "    ax.set_zlim3d(-100, 800);\n",
+    "\n",
+    "    N = range(0, 800, 20)\n",
+    "    pl.contour(X, Y, Z, N, zdir='z', offset=-100, cmap='coolwarm', alpha=1)\n",
+    "    \n",
+    "    ax.plot([-3.89578088] * 2,\n",
+    "            [ 1.19303364] * 2,\n",
+    "            [-100, 4.47697137598], \n",
+    "            color='red', alpha=1, linewidth=1.3, zorder=100, linestyle='dashed',\n",
+    "            label=r'minimum: $J(-3.90, 1.19) = 4.48$')\n",
+    "    ax.scatter([-3.89578088] * 2,\n",
+    "               [ 1.19303364] * 2,\n",
+    "               [-100, 4.47697137598], \n",
+    "               c='r', s=80, marker='x', alpha=1, linewidth=1.3, zorder=100, \n",
+    "               label=r'minimum: $J(-3.90, 1.19) = 4.48$')\n",
+    "    \n",
+    "    if show_gradient:\n",
+    "        ax.plot([3.0, 1.1],\n",
+    "                [3.0, 2.4],\n",
+    "                [263.0, 125.0], \n",
+    "                color='green', alpha=1, linewidth=1.3, zorder=100)\n",
+    "        ax.scatter([3.0],\n",
+    "                   [3.0],\n",
+    "                   [263.0], \n",
+    "                   c='g', s=30, marker='D', alpha=1, linewidth=1.3, zorder=100)\n",
+    "\n",
+    "    ax.margins(0,0,0)\n",
+    "    fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
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+       "<Figure size 691.2x388.8 with 1 Axes>"
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+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "costplot3d(h, x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Na powyższym wykresie poszukiwane minimum funkcji kosztu oznaczone jest czerwonym krzyżykiem.\n",
+    "\n",
+    "Możemy też zobaczyć rzut powyższego trójwymiarowego wykresu na płaszczyznę $(\\theta_0, \\theta_1)$ poniżej:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def costplot2d(hypothesis, x, y, gradient_values=[], nohead=False):\n",
+    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
+    "    ax.set_xlabel(r'$\\theta_0$')\n",
+    "    ax.set_ylabel(r'$\\theta_1$')\n",
+    "    \n",
+    "    j = lambda theta0, theta1: costfun(hypothesis, x, y)([theta0, theta1])\n",
+    "    X = np.arange(-10, 10.1, 0.1)\n",
+    "    Y = np.arange(-1, 4.1, 0.1)\n",
+    "    X, Y = np.meshgrid(X, Y)\n",
+    "    Z = np.array([[J(hypothesis, [theta0, theta1], x, y) \n",
+    "                    for theta0, theta1 in zip(xRow, yRow)] \n",
+    "                   for xRow, yRow in zip(X, Y)])\n",
+    "    \n",
+    "    N = range(0, 800, 20)\n",
+    "    pl.contour(X, Y, Z, N, cmap='coolwarm', alpha=1)\n",
+    "\n",
+    "    ax.scatter([-3.89578088], [1.19303364], c='r', s=80, marker='x',\n",
+    "               label=r'minimum: $J(-3.90, 1.19) = 4.48$')\n",
+    "    \n",
+    "    if len(gradient_values) > 0:\n",
+    "        prev_theta = gradient_values[0][1]\n",
+    "        ax.scatter([prev_theta[0]], [prev_theta[1]],\n",
+    "                   c='g', s=30, marker='D', zorder=100)\n",
+    "        for cost, theta in gradient_values[1:]:\n",
+    "            dtheta = [theta[0] - prev_theta[0], theta[1] - prev_theta[1]]\n",
+    "            ax.arrow(prev_theta[0], prev_theta[1], dtheta[0], dtheta[1], \n",
+    "                     color='green', \n",
+    "                     head_width=(0.0 if nohead else 0.1), \n",
+    "                     head_length=(0.0 if nohead else 0.2),\n",
+    "                     zorder=100)\n",
+    "            prev_theta = theta\n",
+    "    \n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
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+       "<Figure size 691.2x388.8 with 1 Axes>"
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+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = costplot2d(h, x, y)\n",
+    "legend(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Cechy funkcji kosztu\n",
+    "* $J(\\theta)$ jest funkcją wypukłą\n",
+    "* $J(\\theta)$ posiada tylko jedno minimum lokalne"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 2.2. Metoda gradientu prostego"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Metoda gradientu prostego\n",
+    "Metoda znajdowania minimów lokalnych."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Idea:\n",
+    " * Zacznijmy od dowolnego $\\theta$.\n",
+    " * Zmieniajmy powoli $\\theta$ tak, aby zmniejszać $J(\\theta)$, aż w końcu znajdziemy minimum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
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+       "\" style=\"fill:none;stroke:#008000;stroke-linecap:square;stroke-width:1.3;\"/>\r\n",
+       "   </g>\r\n",
+       "  </g>\r\n",
+       " </g>\r\n",
+       " <defs>\r\n",
+       "  <clipPath id=\"p7484bb01c9\">\r\n",
+       "   <rect height=\"367.2\" width=\"669.6\" x=\"7.2\" y=\"7.2\"/>\r\n",
+       "  </clipPath>\r\n",
+       " </defs>\r\n",
+       "</svg>\r\n"
+      ],
+      "text/plain": [
+       "<Figure size 691.2x388.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "costplot3d(h, x, y, show_gradient=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Przykładowe wartości kolejnych przybliżeń (sztuczne)\n",
+    "\n",
+    "gv = [[_, [3.0, 3.0]], [_, [2.6, 2.4]], [_, [2.2, 2.0]], [_, [1.6, 1.6]], [_, [0.4, 1.2]]]\n",
+    "\n",
+    "# Przygotowanie interaktywnego wykresu\n",
+    "\n",
+    "sliderSteps1 = widgets.IntSlider(min=0, max=3, step=1, value=0, description='kroki', width=300)\n",
+    "\n",
+    "def slide4(steps):\n",
+    "    costplot2d(h, x, y, gradient_values=gv[:steps+1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cfbfb43286a44fa9a0aafa2db83176bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=0, description='kroki', max=3), Output()), _dom_classes=('widget-interac…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.slide4(steps)>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "widgets.interact(slide4, steps=sliderSteps1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Metoda gradientu prostego\n",
+    "W każdym kroku będziemy aktualizować parametry $\\theta_j$:\n",
+    "\n",
+    "$$ \\theta_j := \\theta_j - \\alpha \\frac{\\partial}{\\partial \\theta_j} J(\\theta) \\quad \\mbox{ dla każdego } j $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Współczynnik $\\alpha$ nazywamy **długością kroku** lub **współczynnikiem szybkości uczenia** (*learning rate*)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "$$ \\begin{array}{rcl}\n",
+    "\\dfrac{\\partial}{\\partial \\theta_j} J(\\theta)\n",
+    " & = & \\dfrac{\\partial}{\\partial \\theta_j} \\dfrac{1}{2m} \\displaystyle\\sum_{i = 1}^{m} \\left( h_{\\theta} \\left( x^{(i)} \\right) - y^{(i)} \\right) ^2 \\\\\n",
+    " & = & 2 \\cdot \\dfrac{1}{2m} \\displaystyle\\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) \\cdot \\dfrac{\\partial}{\\partial\\theta_j} \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) \\\\\n",
+    " & = & \\dfrac{1}{m}\\displaystyle\\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) - y^{(i)} \\right) \\cdot \\dfrac{\\partial}{\\partial\\theta_j} \\left( \\displaystyle\\sum_{i=0}^n \\theta_i x_i^{(i)} - y^{(i)} \\right)\\\\\n",
+    " & = & \\dfrac{1}{m}\\displaystyle\\sum_{i=1}^m \\left( h_\\theta \\left( x^{(i)} \\right) -y^{(i)} \\right) x_j^{(i)} \\\\\n",
+    "\\end{array} $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Czyli dla regresji liniowej jednej zmiennej:\n",
+    "\n",
+    "$$ h_\\theta(x) = \\theta_0 + \\theta_1x $$\n",
+    "\n",
+    "w każdym kroku będziemy aktualizować:\n",
+    "\n",
+    "$$\n",
+    "\\begin{array}{rcl}\n",
+    "\\theta_0 & := & \\theta_0 - \\alpha \\, \\dfrac{1}{m}\\displaystyle\\sum_{i=1}^m \\left( h_\\theta(x^{(i)})-y^{(i)} \\right) \\\\ \n",
+    "\\theta_1 & := & \\theta_1 - \\alpha \\, \\dfrac{1}{m}\\displaystyle\\sum_{i=1}^m \\left( h_\\theta(x^{(i)})-y^{(i)} \\right) x^{(i)}\\\\ \n",
+    "\\end{array}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "###### Uwaga!\n",
+    " * W każdym kroku aktualizujemy *jednocześnie* $\\theta_0$ i $\\theta_1$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    " * Kolejne kroki wykonujemy aż uzyskamy zbieżność"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Metoda gradientu prostego – implementacja"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Wyświetlanie macierzy w LaTeX-u\n",
+    "\n",
+    "def LatexMatrix(matrix):\n",
+    "    ltx = r'\\left[\\begin{array}'\n",
+    "    m, n = matrix.shape\n",
+    "    ltx += '{' + (\"r\" * n) + '}'\n",
+    "    for i in range(m):\n",
+    "        ltx += r\" & \".join([('%.4f' % j.item()) for j in matrix[i]]) + r\" \\\\ \"\n",
+    "    ltx += r'\\end{array}\\right]'\n",
+    "    return ltx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def gradient_descent(h, cost_fun, theta, x, y, alpha, eps):\n",
+    "    current_cost = cost_fun(h, theta, x, y)\n",
+    "    log = [[current_cost, theta]]  # log przechowuje wartości kosztu i parametrów\n",
+    "    m = len(y)\n",
+    "    while True:\n",
+    "        new_theta = [\n",
+    "            theta[0] - alpha/float(m) * sum(h(theta, x[i]) - y[i]\n",
+    "                                            for i in range(m)),   \n",
+    "            theta[1] - alpha/float(m) * sum((h(theta, x[i]) - y[i]) * x[i]\n",
+    "                                            for i in range(m))]\n",
+    "        theta = new_theta  # jednoczesna aktualizacja - używamy zmiennej tymaczasowej\n",
+    "        try:\n",
+    "            current_cost, prev_cost = cost_fun(h, theta, x, y), current_cost\n",
+    "        except OverflowError:\n",
+    "            break      \n",
+    "        if abs(prev_cost - current_cost) <= eps:\n",
+    "            break     \n",
+    "        log.append([current_cost, theta])\n",
+    "    return theta, log"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\large\\textrm{Wynik:}\\quad \\theta = \\left[\\begin{array}{r}0.0525 \\\\ 0.8015 \\\\ \\end{array}\\right] \\quad J(\\theta) = 6.0273 \\quad \\textrm{po 3604 iteracjach}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "best_theta, log = gradient_descent(h, J, [0.0, 0.0], x, y, alpha=0.00001, eps=0.0001)\n",
+    "\n",
+    "display(Math(r'\\large\\textrm{Wynik:}\\quad \\theta = ' + \n",
+    "             LatexMatrix(np.matrix(best_theta).reshape(2,1)) + \n",
+    "            (r' \\quad J(\\theta) = %.4f' % log[-1][0])  \n",
+    "            + r' \\quad \\textrm{po %d iteracjach}' % len(log))) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Przygotowanie interaktywnego wykresu\n",
+    "\n",
+    "sliderSteps2 = widgets.IntSlider(min=0, max=500, step=1, value=1, description='kroki', width=300)\n",
+    "\n",
+    "def slide5(steps):\n",
+    "    costplot2d(h, x, y, gradient_values=log[:steps+1], nohead=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dae926a503042499b5db05c8a27bae1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=1, description='kroki', max=500), Button(description='Run Interact', sty…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.slide5(steps)>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "widgets.interact_manual(slide5, steps=sliderSteps2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Współczynnik $\\alpha$ (długość kroku)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Tempo zbieżności metody gradientu prostego możemy regulować za pomocą parametru $\\alpha$, pamiętając, że:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    " * Jeżeli długość kroku jest zbyt mała, algorytm może działać zbyt wolno."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    " * Jeżeli długość kroku jest zbyt duża, algorytm może nie być zbieżny."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 2.3. Predykcja wyników"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Zbudowaliśmy model, dzięki któremu wiemy, jaka jest zależność między dochodem firmy transportowej ($y$) a ludnością miasta ($x$).\n",
+    "\n",
+    "Wróćmy teraz do postawionego na początku wykładu pytania: jak przewidzieć dochód firmy transportowej w mieście o danej wielkości?\n",
+    "\n",
+    "Odpowiedź polega po prostu na zastosowaniu funkcji $h$ z wyznaczonymi w poprzednim kroku parametrami $\\theta$.\n",
+    "\n",
+    "Na przykład, jeżeli miasto ma $536\\,000$ ludności, to $x = 53.6$ (bo dane trenujące były wyrażone w dziesiątkach tysięcy mieszkańców, a $536\\,000 = 53.6 \\cdot 10\\,000$) i możemy użyć znalezionych parametrów $\\theta$, by wykonać następujące obliczenia:\n",
+    "$$ \\hat{y} \\, = \\, h_\\theta(x) \\, = \\, \\theta_0 + \\theta_1 \\, x \\, = \\, 0.0494 + 0.7591 \\cdot 53.6 \\, = \\, 40.7359 $$\n",
+    "\n",
+    "Czyli używając zdefiniowanych wcześniej funkcji:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "43.01265462001823\n"
+     ]
+    }
+   ],
+   "source": [
+    "example_x = 53.6\n",
+    "predicted_y = h(best_theta, example_x)\n",
+    "print(predicted_y)  ## taki jest przewidywany dochód tej firmy transportowej w 536-tysięcznym mieście"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 2.4. Ewaluacja modelu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Jak ocenić jakość stworzonego przez nas modelu?\n",
+    "\n",
+    " * Trzeba sprawdzić, jak przewidywania modelu zgadzają się z oczekiwaniami!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Czy możemy w tym celu użyć danych, których użyliśmy do wytrenowania modelu?\n",
+    "**NIE!**\n",
+    "\n",
+    " * Istotą uczenia maszynowego jest budowanie modeli/algorytmów, które dają dobre przewidywania dla **nieznanych** danych – takich, z którymi algorytm nie miał jeszcze styczności! Nie sztuką jest przewidywać rzeczy, które juz sie zna.\n",
+    " * Dlatego testowanie/ewaluowanie modelu na zbiorze uczącym mija się z celem i jest nieprzydatne.\n",
+    " * Do ewaluacji modelu należy użyć oddzielnego zbioru danych.\n",
+    " * **Dane uczące i dane testowe zawsze powinny stanowić oddzielne zbiory!**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Na wykładzie *5. Dobre praktyki w uczeniu maszynowym* dowiesz się, jak podzielić posiadane dane na zbiór uczący i zbiór testowy.\n",
+    "\n",
+    "Tutaj, na razie, do ewaluacji użyjemy specjalnie przygotowanego zbioru testowego."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Jako metrykę ewaluacji wykorzystamy znany nam już błąd średniokwadratowy (RMSE):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def rmse(expected, predicted):\n",
+    "    \"\"\"Błąd średniokwadratowy\"\"\"\n",
+    "    m = len(expected)\n",
+    "    if len(predicted) != m:\n",
+    "        raise Exception('Wektory mają różne długości!')\n",
+    "    return 1.0 / (2 * m) * sum((expected[i] - predicted[i])**2 for i in range(m))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.283571974307861\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Wczytwanie danych testowych z pliku za pomocą numpy\n",
+    "\n",
+    "test_data = np.loadtxt('data01_test.csv', delimiter=',')\n",
+    "x_test = test_data[:, 0]\n",
+    "y_test = test_data[:, 1]\n",
+    "\n",
+    "# Obliczenie przewidywań modelu\n",
+    "y_pred = h(best_theta, x_test)\n",
+    "\n",
+    "# Obliczenie RMSE na zbiorze testowym (im mniejszy RMSE, tym lepiej!)\n",
+    "evaluation_result = rmse(y_test, y_pred)\n",
+    "\n",
+    "print(evaluation_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Otrzymana wartość mówi nam o tym, jak dobry jest stworzony przez nas model.\n",
+    "\n",
+    "W przypadku metryki RMSE im mniejsz wartość, tym lepiej.\n",
+    "\n",
+    "W ten sposób możemy np. porównywać różne modele."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 2.5. Regresja liniowa wielu zmiennych"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Do przewidywania wartości $y$ możemy użyć więcej niż jednej cechy $x$:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Przykład – ceny mieszkań"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "y : price    x1: isNew    x2: rooms    x3: floor    x4: location x5: sqrMetres\n",
+      "476118.0     False        3            1            Centrum      78          \n",
+      "459531.0     False        3            2            Sołacz       62          \n",
+      "411557.0     False        3            0            Sołacz       15          \n",
+      "496416.0     False        4            0            Sołacz       14          \n",
+      "406032.0     False        3            0            Sołacz       15          \n",
+      "450026.0     False        3            1            Naramowice   80          \n",
+      "571229.15    False        2            4            Wilda        39          \n",
+      "325000.0     False        3            1            Grunwald     54          \n",
+      "268229.0     False        2            1            Grunwald     90          \n"
+     ]
+    }
+   ],
+   "source": [
+    "import csv\n",
+    "\n",
+    "reader = csv.reader(open('data02_train.tsv', encoding='utf-8'), delimiter='\\t')\n",
+    "for i, row in enumerate(list(reader)[:10]):\n",
+    "    if i == 0:\n",
+    "        print(' '.join(['{}: {:8}'.format('x' + str(j) if j > 0 else 'y ', entry)\n",
+    "                        for j, entry in enumerate(row)]))\n",
+    "    else:\n",
+    "        print(' '.join(['{:12}'.format(entry) for entry in row]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$ x^{(2)} = ({\\rm \"False\"}, 3, 2, {\\rm \"Sołacz\"}, 62), \\quad x_3^{(2)} = 2 $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Hipoteza"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "W naszym przypadku (wybraliśmy 5 cech):\n",
+    "\n",
+    "$$ h_\\theta(x) = \\theta_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\theta_3 x_3 + \\theta_4 x_4 + \\theta_5 x_5 $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "W ogólności ($n$ cech):\n",
+    "\n",
+    "$$ h_\\theta(x) = \\theta_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\ldots + \\theta_n x_n $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Jeżeli zdefiniujemy $x_0 = 1$, będziemy mogli powyższy wzór zapisać w bardziej kompaktowy sposób:\n",
+    "\n",
+    "$$\n",
+    "\\begin{array}{rcl}\n",
+    "h_\\theta(x)\n",
+    " & = & \\theta_0 x_0 + \\theta_1 x_1 + \\theta_2 x_2 + \\ldots + \\theta_n x_n \\\\\n",
+    " & = & \\displaystyle\\sum_{i=0}^{n} \\theta_i x_i \\\\\n",
+    " & = & \\theta^T \\, x \\\\\n",
+    " & = & x^T \\, \\theta \\\\\n",
+    "\\end{array}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Metoda gradientu prostego – notacja macierzowa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Metoda gradientu prostego przyjmie bardzo elegancką formę, jeżeli do jej zapisu użyjemy wektorów i macierzy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$\n",
+    "X=\\left[\\begin{array}{cc}\n",
+    "1 & \\left( \\vec x^{(1)} \\right)^T \\\\\n",
+    "1 & \\left( \\vec x^{(2)} \\right)^T \\\\\n",
+    "\\vdots & \\vdots\\\\\n",
+    "1 & \\left( \\vec x^{(m)} \\right)^T \\\\\n",
+    "\\end{array}\\right] \n",
+    "= \\left[\\begin{array}{cccc}\n",
+    "1 & x_1^{(1)} & \\cdots & x_n^{(1)} \\\\\n",
+    "1 & x_1^{(2)} & \\cdots & x_n^{(2)} \\\\\n",
+    "\\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "1 & x_1^{(m)} & \\cdots & x_n^{(m)} \\\\\n",
+    "\\end{array}\\right]\n",
+    "\\quad\n",
+    "\\vec{y} = \n",
+    "\\left[\\begin{array}{c}\n",
+    "y^{(1)}\\\\\n",
+    "y^{(2)}\\\\\n",
+    "\\vdots\\\\\n",
+    "y^{(m)}\\\\\n",
+    "\\end{array}\\right]\n",
+    "\\quad\n",
+    "\\theta = \\left[\\begin{array}{c}\n",
+    "\\theta_0\\\\\n",
+    "\\theta_1\\\\\n",
+    "\\vdots\\\\\n",
+    "\\theta_n\\\\\n",
+    "\\end{array}\\right]\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Wersje macierzowe funkcji rysowania wykresów punktowych oraz krzywej regresyjnej\n",
+    "\n",
+    "def hMx(theta, X):\n",
+    "    return X * theta\n",
+    "\n",
+    "def regdotsMx(X, y):    \n",
+    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
+    "    ax.scatter([X[:, 1]], [y], c='r', s=50, label='Dane')\n",
+    "    \n",
+    "    ax.set_xlabel('Populacja')\n",
+    "    ax.set_ylabel('Zysk')\n",
+    "    ax.margins(.05, .05)\n",
+    "    pl.ylim(y.min() - 1, y.max() + 1)\n",
+    "    pl.xlim(np.min(X[:, 1]) - 1, np.max(X[:, 1]) + 1)\n",
+    "    return fig\n",
+    "\n",
+    "def reglineMx(fig, fun, theta, X):\n",
+    "    ax = fig.axes[0]\n",
+    "    x0, x1 = np.min(X[:, 1]), np.max(X[:, 1])\n",
+    "    L = [x0, x1]\n",
+    "    LX = np.matrix([1, x0, 1, x1]).reshape(2, 2)\n",
+    "    ax.plot(L, fun(theta, LX), linewidth='2',\n",
+    "            label=(r'$y={theta0:.2}{op}{theta1:.2}x$'.format(\n",
+    "                theta0=float(theta[0][0]),\n",
+    "                theta1=(float(theta[1][0]) if theta[1][0] >= 0 else float(-theta[1][0])),\n",
+    "                op='+' if theta[1][0] >= 0 else '-')))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.  3.  1. 78.]\n",
+      " [ 1.  3.  2. 62.]\n",
+      " [ 1.  3.  0. 15.]\n",
+      " [ 1.  4.  0. 14.]\n",
+      " [ 1.  3.  0. 15.]]\n",
+      "(1339, 4)\n",
+      "\n",
+      "[[476118.]\n",
+      " [459531.]\n",
+      " [411557.]\n",
+      " [496416.]\n",
+      " [406032.]]\n",
+      "(1339, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Wczytwanie danych z pliku za pomocą numpy – regresja liniowa wielu zmiennych – notacja macierzowa\n",
+    "\n",
+    "import pandas\n",
+    "\n",
+    "data = pandas.read_csv('data02_train.tsv', delimiter='\\t', usecols=['price', 'rooms', 'floor', 'sqrMetres'])\n",
+    "m, n_plus_1 = data.values.shape\n",
+    "n = n_plus_1 - 1\n",
+    "Xn = data.values[:, 1:].reshape(m, n)\n",
+    "\n",
+    "# Dodaj kolumnę jedynek do macierzy\n",
+    "XMx = np.matrix(np.concatenate((np.ones((m, 1)), Xn), axis=1)).reshape(m, n_plus_1)\n",
+    "yMx = np.matrix(data.values[:, 0]).reshape(m, 1)\n",
+    "\n",
+    "print(XMx[:5])\n",
+    "print(XMx.shape)\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "print(yMx[:5])\n",
+    "print(yMx.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Funkcja kosztu – notacja macierzowa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$J(\\theta)=\\dfrac{1}{2|\\vec y|}\\left(X\\theta-\\vec{y}\\right)^T\\left(X\\theta-\\vec{y}\\right)$$ \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\Large J(\\theta) = 85104141370.9717$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display, Math, Latex\n",
+    "\n",
+    "def JMx(theta,X,y):\n",
+    "    \"\"\"Wersja macierzowa funkcji kosztu\"\"\"\n",
+    "    m = len(y)\n",
+    "    J = 1.0 / (2.0 * m) * ((X * theta - y) . T * ( X * theta - y))\n",
+    "    return J.item()\n",
+    "\n",
+    "thetaMx = np.matrix([10, 90, -1, 2.5]).reshape(4, 1) \n",
+    "\n",
+    "cost = JMx(thetaMx,XMx,yMx) \n",
+    "display(Math(r'\\Large J(\\theta) = %.4f' % cost))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Gradient – notacja macierzowa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$\\nabla J(\\theta) = \\frac{1}{|\\vec y|} X^T\\left(X\\theta-\\vec y\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\large \\theta = \\left[\\begin{array}{r}10.0000 \\\\ 90.0000 \\\\ -1.0000 \\\\ 2.5000 \\\\ \\end{array}\\right]\\quad\\large \\nabla J(\\theta) = \\left[\\begin{array}{r}-373492.7442 \\\\ -1075656.5086 \\\\ -989554.4921 \\\\ -23806475.6561 \\\\ \\end{array}\\right]$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display, Math, Latex\n",
+    "\n",
+    "def dJMx(theta,X,y):\n",
+    "    \"\"\"Wersja macierzowa gradientu funckji kosztu\"\"\"\n",
+    "    return 1.0 / len(y) * (X.T * (X * theta - y)) \n",
+    "\n",
+    "thetaMx = np.matrix([10, 90, -1, 2.5]).reshape(4, 1) \n",
+    "\n",
+    "display(Math(r'\\large \\theta = ' + LatexMatrix(thetaMx) + \n",
+    "             r'\\quad' + r'\\large \\nabla J(\\theta) = ' \n",
+    "             + LatexMatrix(dJMx(thetaMx,XMx,yMx))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Algorytm gradientu prostego – notacja macierzowa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$ \\theta := \\theta - \\alpha \\, \\nabla J(\\theta) $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\large\\textrm{Wynik:}\\quad \\theta = \\left[\\begin{array}{r}17446.2104 \\\\ 86476.7968 \\\\ -1374.8949 \\\\ 2165.0689 \\\\ \\end{array}\\right] \\quad J(\\theta) = 10324864803.0591 \\quad \\textrm{po 374576 iteracjach}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Implementacja algorytmu gradientu prostego za pomocą numpy i macierzy\n",
+    "\n",
+    "def GDMx(fJ, fdJ, theta, X, y, alpha, eps):\n",
+    "    current_cost = fJ(theta, X, y)\n",
+    "    log = [[current_cost, theta]]\n",
+    "    while True:\n",
+    "        theta = theta - alpha * fdJ(theta, X, y) # implementacja wzoru\n",
+    "        current_cost, prev_cost = fJ(theta, X, y), current_cost\n",
+    "        if abs(prev_cost - current_cost) <= eps:\n",
+    "            break\n",
+    "        if current_cost > prev_cost:\n",
+    "            print('Długość kroku (alpha) jest zbyt duża!')\n",
+    "            break\n",
+    "        log.append([current_cost, theta])\n",
+    "    return theta, log\n",
+    "\n",
+    "thetaStartMx = np.zeros((n + 1, 1))\n",
+    "\n",
+    "# Zmieniamy wartości alpha (rozmiar kroku) oraz eps (kryterium stopu)\n",
+    "thetaBestMx, log = GDMx(JMx, dJMx, thetaStartMx, \n",
+    "                        XMx, yMx, alpha=0.0001, eps=0.1)\n",
+    "\n",
+    "######################################################################\n",
+    "display(Math(r'\\large\\textrm{Wynik:}\\quad \\theta = ' + \n",
+    "             LatexMatrix(thetaBestMx) + \n",
+    "             (r' \\quad J(\\theta) = %.4f' % log[-1][0])  \n",
+    "             + r' \\quad \\textrm{po %d iteracjach}' % len(log)))  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 2.6. Metoda gradientu prostego w praktyce"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Kryterium stopu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Algorytm gradientu prostego polega na wykonywaniu określonych kroków w pętli. Pytanie brzmi: kiedy należy zatrzymać wykonywanie tej pętli?\n",
+    "\n",
+    "W każdej kolejnej iteracji wartość funkcji kosztu maleje o coraz mniejszą wartość.\n",
+    "Parametr `eps` określa, jaka wartość graniczna tej różnicy jest dla nas wystarczająca:\n",
+    "\n",
+    " * Im mniejsza wartość `eps`, tym dokładniejszy wynik, ale dłuższy czas działania algorytmu.\n",
+    " * Im większa wartość `eps`, tym krótszy czas działania algorytmu, ale mniej dokładny wynik."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Na wykresie zobaczymy porównanie regresji dla różnych wartości `eps`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
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+       "    </defs>\r\n",
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+       "    </g>\r\n",
+       "   </g>\r\n",
+       "  </g>\r\n",
+       " </g>\r\n",
+       " <defs>\r\n",
+       "  <clipPath id=\"pf109671648\">\r\n",
+       "   <rect height=\"311.04\" width=\"552.96\" x=\"40.603125\" y=\"9.413144\"/>\r\n",
+       "  </clipPath>\r\n",
+       " </defs>\r\n",
+       "</svg>\r\n"
+      ],
+      "text/plain": [
+       "<Figure size 691.2x388.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Wczytwanie danych z pliku za pomocą numpy – wersja macierzowa\n",
+    "data = np.loadtxt('data01_train.csv', delimiter=',')\n",
+    "m, n_plus_1 = data.shape\n",
+    "n = n_plus_1 - 1\n",
+    "Xn = data[:, 0:n].reshape(m, n)\n",
+    "\n",
+    "# Dodaj kolumnę jedynek do macierzy\n",
+    "XMx = np.matrix(np.concatenate((np.ones((m, 1)), Xn), axis=1)).reshape(m, n_plus_1)\n",
+    "yMx = np.matrix(data[:, 1]).reshape(m, 1)\n",
+    "\n",
+    "thetaStartMx = np.zeros((2, 1))\n",
+    "\n",
+    "fig = regdotsMx(XMx, yMx)\n",
+    "theta_e1, log1 = GDMx(JMx, dJMx, thetaStartMx, XMx, yMx, alpha=0.01, eps=0.01)  # niebieska linia\n",
+    "reglineMx(fig, hMx, theta_e1, XMx)\n",
+    "theta_e2, log2 = GDMx(JMx, dJMx, thetaStartMx, XMx, yMx, alpha=0.01, eps=0.000001)  # pomarańczowa linia\n",
+    "reglineMx(fig, hMx, theta_e2, XMx)\n",
+    "legend(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\theta_{10^{-2}} = \\left[\\begin{array}{r}0.0531 \\\\ 0.8365 \\\\ \\end{array}\\right]\\quad\\theta_{10^{-6}} = \\left[\\begin{array}{r}-3.4895 \\\\ 1.1786 \\\\ \\end{array}\\right]$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'\\theta_{10^{-2}} = ' + LatexMatrix(theta_e1) +\n",
+    "             r'\\quad\\theta_{10^{-6}} = ' + LatexMatrix(theta_e2)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Długość kroku ($\\alpha$)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Jak zmienia się koszt w kolejnych krokach w zależności od alfa\n",
+    "\n",
+    "def costchangeplot(logs):\n",
+    "    fig = pl.figure(figsize=(16*.6, 9*.6))\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
+    "    ax.set_xlabel('krok')\n",
+    "    ax.set_ylabel(r'$J(\\theta)$')\n",
+    "\n",
+    "    X = np.arange(0, 500, 1)\n",
+    "    Y = [logs[step][0] for step in X]\n",
+    "    ax.plot(X, Y, linewidth='2', label=(r'$J(\\theta)$'))\n",
+    "    return fig\n",
+    "\n",
+    "def slide7(alpha):\n",
+    "    best_theta, log = gradient_descent(h, J, [0.0, 0.0], x, y, alpha=alpha, eps=0.0001)\n",
+    "    fig = costchangeplot(log)\n",
+    "    legend(fig)\n",
+    "\n",
+    "sliderAlpha1 = widgets.FloatSlider(min=0.01, max=0.03, step=0.001, value=0.02, description=r'$\\alpha$', width=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fbb851370a8c4e828554c3bfa27b3746",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.02, description='$\\\\alpha$', max=0.03, min=0.01, step=0.001), Button…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.slide7(alpha)>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "widgets.interact_manual(slide7, alpha=sliderAlpha1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## 2.7. Normalizacja danych"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Normalizacja danych to proces, który polega na dostosowaniu danych wejściowych w taki sposób, żeby ułatwić działanie algorytmowi gradientu prostego.\n",
+    "\n",
+    "Wyjaśnię to na przykladzie."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Użyjemy danych z „Gratka flats challenge 2017”.\n",
+    "\n",
+    "Rozważmy model $h(x) = \\theta_0 + \\theta_1 x_1 + \\theta_2 x_2$, w którym cena mieszkania prognozowana jest na podstawie liczby pokoi $x_1$ i metrażu $x_2$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>price</th>\n",
+       "      <th>rooms</th>\n",
+       "      <th>sqrMetres</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>476118.00</td>\n",
+       "      <td>3</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>459531.00</td>\n",
+       "      <td>3</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>411557.00</td>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>496416.00</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>406032.00</td>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>450026.00</td>\n",
+       "      <td>3</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>571229.15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>325000.00</td>\n",
+       "      <td>3</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>268229.00</td>\n",
+       "      <td>2</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>604836.00</td>\n",
+       "      <td>4</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       price  rooms  sqrMetres\n",
+       "0  476118.00      3         78\n",
+       "1  459531.00      3         62\n",
+       "2  411557.00      3         15\n",
+       "3  496416.00      4         14\n",
+       "4  406032.00      3         15\n",
+       "5  450026.00      3         80\n",
+       "6  571229.15      2         39\n",
+       "7  325000.00      3         54\n",
+       "8  268229.00      2         90\n",
+       "9  604836.00      4         40"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Wczytanie danych przy pomocy biblioteki pandas\n",
+    "import pandas\n",
+    "alldata = pandas.read_csv('data_flats.tsv', header=0, sep='\\t',\n",
+    "                          usecols=['price', 'rooms', 'sqrMetres'])\n",
+    "alldata[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Funkcja, która pokazuje wartości minimalne i maksymalne w macierzy X\n",
+    "\n",
+    "def show_mins_and_maxs(XMx):\n",
+    "    mins = np.amin(XMx, axis=0).tolist()[0]  # wartości minimalne\n",
+    "    maxs = np.amax(XMx, axis=0).tolist()[0]  # wartości maksymalne\n",
+    "    for i, (xmin, xmax) in enumerate(zip(mins, maxs)):\n",
+    "        display(Math(\n",
+    "            r'${:.2F} \\leq x_{} \\leq {:.2F}$'.format(xmin, i, xmax)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Przygotowanie danych\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "data2 = np.matrix(alldata[['rooms', 'sqrMetres', 'price']])\n",
+    "\n",
+    "m, n_plus_1 = data2.shape\n",
+    "n = n_plus_1 - 1\n",
+    "Xn = data2[:, 0:n]\n",
+    "\n",
+    "XMx2 = np.matrix(np.concatenate((np.ones((m, 1)), Xn), axis=1)).reshape(m, n_plus_1)\n",
+    "yMx2 = np.matrix(data2[:, -1]).reshape(m, 1) / 1000.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Cechy w danych treningowych przyjmują wartości z zakresu:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 1.00 \\leq x_0 \\leq 1.00$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 2.00 \\leq x_1 \\leq 7.00$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 12.00 \\leq x_2 \\leq 196.00$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_mins_and_maxs(XMx2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Jak widzimy, $x_2$ przyjmuje wartości dużo większe niż $x_1$.\n",
+    "Powoduje to, że wykres funkcji kosztu jest bardzo „spłaszczony” wzdłuż jednej z osi:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def contour_plot(X, y, rescale=10**8):\n",
+    "    theta0_vals = np.linspace(-100000, 100000, 100)\n",
+    "    theta1_vals = np.linspace(-100000, 100000, 100)\n",
+    "\n",
+    "    J_vals = np.zeros(shape=(theta0_vals.size, theta1_vals.size))\n",
+    "    for t1, element in enumerate(theta0_vals):\n",
+    "        for t2, element2 in enumerate(theta1_vals):\n",
+    "            thetaT = np.matrix([1.0, element, element2]).reshape(3,1)\n",
+    "            J_vals[t1, t2] = JMx(thetaT, X, y) / rescale\n",
+    "            \n",
+    "    pl.figure()\n",
+    "    pl.contour(theta0_vals, theta1_vals, J_vals.T, np.logspace(-2, 3, 20))\n",
+    "    pl.xlabel(r'$\\theta_1$')\n",
+    "    pl.ylabel(r'$\\theta_2$')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "</svg>\r\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "contour_plot(XMx2, yMx2, rescale=10**10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Jeżeli funkcja kosztu ma kształt taki, jak na powyższym wykresie, to łatwo sobie wyobrazić, że znalezienie minimum lokalnego przy użyciu metody gradientu prostego musi stanowć nie lada wyzwanie: algorytm szybko znajdzie „rynnę”, ale „zjazd” wzdłuż „rynny” w poszukiwaniu minimum będzie odbywał się bardzo powoli.\n",
+    "\n",
+    "Jak temu zaradzić?\n",
+    "\n",
+    "Spróbujemy przekształcić dane tak, żeby funkcja kosztu miała „ładny”, regularny kształt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Skalowanie"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Będziemy dążyć do tego, żeby każda z cech przyjmowała wartości w podobnym zakresie.\n",
+    "\n",
+    "W tym celu przeskalujemy wartości każdej z cech, dzieląc je przez wartość maksymalną:\n",
+    "\n",
+    "$$ \\hat{x_i}^{(j)} := \\frac{x_i^{(j)}}{\\max_j x_i^{(j)}} $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 1.00 \\leq x_0 \\leq 1.00$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 0.29 \\leq x_1 \\leq 1.00$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 0.06 \\leq x_2 \\leq 1.00$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "XMx2_scaled = XMx2 / np.amax(XMx2, axis=0)\n",
+    "\n",
+    "show_mins_and_maxs(XMx2_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
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+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "contour_plot(XMx2_scaled, yMx2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Normalizacja średniej"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "Będziemy dążyć do tego, żeby dodatkowo średnia wartość każdej z cech była w okolicach $0$.\n",
+    "\n",
+    "W tym celu oprócz przeskalowania odejmiemy wartość średniej od wartości każdej z cech:\n",
+    "\n",
+    "$$ \\hat{x_i}^{(j)} := \\frac{x_i^{(j)} - \\mu_i}{\\max_j x_i^{(j)}} $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 0.00 \\leq x_0 \\leq 0.00$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle -0.10 \\leq x_1 \\leq 0.62$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle -0.23 \\leq x_2 \\leq 0.70$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "XMx2_norm = (XMx2 - np.mean(XMx2, axis=0)) / np.amax(XMx2, axis=0)\n",
+    "\n",
+    "show_mins_and_maxs(XMx2_norm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
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+       "   </g>\r\n",
+       "   <g id=\"LineCollection_10\"/>\r\n",
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+       "   </g>\r\n",
+       "  </g>\r\n",
+       " </g>\r\n",
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+       " </defs>\r\n",
+       "</svg>\r\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "contour_plot(XMx2_norm, yMx2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "notes"
+    }
+   },
+   "source": [
+    "Teraz funkcja kosztu ma wykres o bardzo regularnym kształcie – algorytm gradientu prostego zastosowany w takim przypadku bardzo szybko znajdzie minimum funkcji kosztu."
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  },
+  "livereveal": {
+   "start_slideshow_at": "selected",
+   "theme": "white"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/w1/data01.csv b/wyk/data01_train.csv
similarity index 82%
rename from w1/data01.csv
rename to wyk/data01_train.csv
index 0f88ccb..ab04fdf 100644
--- a/w1/data01.csv
+++ b/wyk/data01_train.csv
@@ -78,20 +78,3 @@
 5.0365,5.7014
 10.274,6.7526
 5.1077,2.0576
-5.7292,0.47953
-5.1884,0.20421
-6.3557,0.67861
-9.7687,7.5435
-6.5159,5.3436
-8.5172,4.2415
-9.1802,6.7981
-6.002,0.92695
-5.5204,0.152
-5.0594,2.8214
-5.7077,1.8451
-7.6366,4.2959
-5.8707,7.2029
-5.3054,1.9869
-8.2934,0.14454
-13.394,9.0551
-5.4369,0.61705
diff --git a/w1/data02.tsv b/wyk/data02_train.tsv
similarity index 79%
rename from w1/data02.tsv
rename to wyk/data02_train.tsv
index 151a3c8..0819da9 100644
--- a/w1/data02.tsv
+++ b/wyk/data02_train.tsv
@@ -1338,338 +1338,3 @@ price	isNew	rooms	floor	location	sqrMetres
 234000.0	True	2	7	Wilda	50
 210000.0	True	2	1	Wilda	65
 279000.0	True	2	2	Łazarz	36
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-249641.0	False	2	0	Ogrody	54
-302887.0	False	2	4	Górczyn	25
-242731.0	False	2	0	Podolany	74
-991380.0	False	5	1	Starołęka	94
-265000.0	True	2	2	Piątkowo	70
-344000.0	True	2	0	Stare	49
-313000.0	True	2	4	Stare	49
-315000.0	True	3	0	Stare	60
-315000.0	True	3	0	Rataje	63
-690000.0	True	2	1	Stare	20
-465000.0	True	2	2	Wilda	49
-368110.0	True	3	2	Nowe	64
-282994.0	True	2	0	Nowe	55
-350000.0	True	2	3	Rataje	52
-780000.0	True	3	0	Grunwald	87
-263000.0	True	2	0	Rataje	38
-219000.0	True	2	7	Winogrady	38
-250000.0	True	3	3	Rataje	53
-394000.0	True	4	0	Nowe	88
-535000.0	True	2	2	Centrum	55
-329000.0	True	3	13	Rataje	65
-192750.0	True	2	2	Nowe	55
-375408.0	True	3	0	Nowe	88
-649400.0	True	3	4	Nowe	50
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-290646.0	True	2	2	Stare	80
-285678.0	False	2	1	Winogrady	42
-156085.0	False	2	4	Nowe	54
-496573.0	False	3	2	Stare	78
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-380931.0	False	3	3	Starołęka	82
-285200.0	True	2	0	Stare	46
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-399000.0	True	3	4	Piątkowo	69
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-240000.0	True	2	2	Dębiec	48
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-410000.0	True	2	4	Jeżyce	43
-360000.0	True	3	3	Jeżyce	62
-590000.0	True	3	3	Jeżyce	80
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-345240.0	False	4	3	Winogrady	54
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-336969.98	False	2	10	Grunwald	58
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-224343.0	False	2	4	Starołęka	35
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-156085.0	False	2	4	Nowe	54
-285200.0	False	2	1	Winogrady	46
-211190.0	False	2	0	Podolany	78
-309000.0	True	3	3	Wilda	80
-313000.0	True	2	1	Górczyn	48
-313000.0	True	2	1	Dębiec	48
-643720.0	True	4	0	Stare	60
-415000.0	True	2	5	Nowe	53
-325000.0	True	2	3	Stare	60
-599000.0	True	4	3	Stare	97
-319000.0	True	2	0	Grunwald	20
-299000.0	True	3	6	Stare	63
-299000.0	True	2	0	Grunwald	51
-339000.0	True	2	2	Centrum	53
-320000.0	True	3	4	Stare	65
-364000.0	True	3	1	Nowe	67
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