From 5c4f4b2d432a1b4477c4eb0538522a1bba2ee0bf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Sk=C3=B3rzewski?= <pawel.skorzewski@amu.edu.pl>
Date: Thu, 25 Jan 2024 15:30:15 +0100
Subject: [PATCH] =?UTF-8?q?Uzupe=C5=82nienie=20wyk=C5=82adu=2013.=20CNN?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 wyk/13_CNN.ipynb | 80 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)

diff --git a/wyk/13_CNN.ipynb b/wyk/13_CNN.ipynb
index 8846949..4e4605e 100644
--- a/wyk/13_CNN.ipynb
+++ b/wyk/13_CNN.ipynb
@@ -8,6 +8,7 @@
     }
    },
    "source": [
+    "### Uczenie maszynowe\n",
     "# 13. Splotowe sieci neuronowe"
    ]
   },
@@ -97,6 +98,17 @@
     "* nie wykrywa własności „lokalnych” wejścia"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-F.png\"/>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -110,6 +122,28 @@
     "W tym celu tworzymy mniejszą sieć neuronową (mniej neuronów wejściowych) i _kopiujemy_ ją tak, żeby każda jej kopia działała na pewnym fragmencie wejścia (fragmenty mogą nachodzić na siebie)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-Conv2.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-Conv3.png\"/>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -123,6 +157,39 @@
     "Warstw konwolucyjnych może być więcej niż jedna."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" width=\"60%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv-9-Conv2Conv2.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" width=\"50%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv2-9x5-Conv2.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<img style=\"margin: auto\" width=\"50%\" src=\"http://colah.github.io/posts/2014-07-Conv-Nets-Modular/img/Conv2-9x5-Conv2Conv2.png\"/>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -152,6 +219,19 @@
     "Więcej: https://en.wikipedia.org/wiki/Kernel_(image_processing)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "Ilustracja działania funkcji splotu:\n",
+    "\n",
+    "<img style=\"margin: auto\" height=\"80%\" src=\"https://devblogs.nvidia.com/wp-content/uploads/2015/11/Convolution_schematic.gif\"/>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {