From 7f80072aeade59f7e63c837721df4c924227bb57 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pawe=C5=82=20Sk=C3=B3rzewski?= Date: Mon, 23 Jan 2023 16:01:45 +0100 Subject: [PATCH] =?UTF-8?q?Wyk=C5=82ady=2013-15=20-=20poprawki?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- wyk/13_CNN.ipynb | 17 ++++---- wyk/14_RNN.ipynb | 52 +++++++++++++----------- wyk/15_Uczenie_przez_wzmacnianie.ipynb | 19 +++++---- wyk/Autoencoder_schema.png | Bin 0 -> 39550 bytes wyk/Autoencoder_structure.png | Bin 0 -> 49621 bytes wyk/Gated_Recurrent_Unit.png | Bin 0 -> 54606 bytes wyk/Long_Short-Term_Memory.png | Bin 0 -> 57132 bytes wyk/Max_pooling.png | Bin 0 -> 25791 bytes wyk/Recurrent_neural_network_unfold.png | Bin 0 -> 31523 bytes wyk/Typical_cnn.png | Bin 0 -> 119343 bytes wyk/agent_i_srodowisko.png | Bin 0 -> 39659 bytes wyk/paradygmaty_um.png | Bin 0 -> 64208 bytes wyk/system_dialogowy.png | Bin 0 -> 55665 bytes 13 files changed, 48 insertions(+), 40 deletions(-) create mode 100644 wyk/Autoencoder_schema.png create mode 100644 wyk/Autoencoder_structure.png create mode 100644 wyk/Gated_Recurrent_Unit.png create mode 100644 wyk/Long_Short-Term_Memory.png create mode 100644 wyk/Max_pooling.png create mode 100644 wyk/Recurrent_neural_network_unfold.png create mode 100644 wyk/Typical_cnn.png create mode 100644 wyk/agent_i_srodowisko.png create mode 100644 wyk/paradygmaty_um.png create mode 100644 wyk/system_dialogowy.png diff --git a/wyk/13_CNN.ipynb b/wyk/13_CNN.ipynb index 547e7d4..8c9b2a8 100644 --- a/wyk/13_CNN.ipynb +++ b/wyk/13_CNN.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -194,7 +193,7 @@ "\n", "Żeby zredukować liczbę parametrów, a dzięki temu uprościć obliczenia, stosuje się warstwy ***pooling***.\n", "\n", - "*Pooling* to rodzaj próbkowania. Najpopularniejszą jego odmianą jest *max-pooling*, czyli wybieranie najwyższej wartości spośród kilku sąsiadujących pikseli (rys. 12.1)." + "*Pooling* to rodzaj próbkowania. Najpopularniejszą jego odmianą jest *max-pooling*, czyli wybieranie najwyższej wartości spośród kilku sąsiadujących pikseli (rys. 13.1)." ] }, { @@ -205,9 +204,9 @@ } }, "source": [ - "![Rys. 12.1. Pooling](Max_pooling.png \"Rys. 12.1. Pooling\")\n", + "![Rys. 13.1. Pooling](Max_pooling.png \"Rys. 13.1. Pooling\")\n", "\n", - "Rys. 12.1. - źródło: [Aphex34](https://commons.wikimedia.org/wiki/File:Max_pooling.png), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" + "Rys. 13.1. - źródło: [Aphex34](https://commons.wikimedia.org/wiki/File:Max_pooling.png), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" ] }, { @@ -218,7 +217,7 @@ } }, "source": [ - "Warstwy _pooling_ i konwolucyjne można przeplatać ze sobą (rys. 12.2)." + "Warstwy _pooling_ i konwolucyjne można przeplatać ze sobą (rys. 13.2)." ] }, { @@ -229,9 +228,9 @@ } }, "source": [ - "![Rys. 12.2. CNN](Typical_cnn.png \"Rys. 12.2. CNN\")\n", + "![Rys. 13.2. CNN](Typical_cnn.png \"Rys. 13.2. CNN\")\n", "\n", - "Rys. 12.2. - źródło: [Aphex34](https://commons.wikimedia.org/wiki/File:Typical_cnn.png), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" + "Rys. 13.2. - źródło: [Aphex34](https://commons.wikimedia.org/wiki/File:Typical_cnn.png), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" ] }, { @@ -580,7 +579,7 @@ "celltoolbar": "Slideshow", "email": "pawel.skorzewski@amu.edu.pl", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -595,7 +594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]" + "version": "3.10.6" }, "livereveal": { "start_slideshow_at": "selected", diff --git a/wyk/14_RNN.ipynb b/wyk/14_RNN.ipynb index af3fab9..6d0030e 100644 --- a/wyk/14_RNN.ipynb +++ b/wyk/14_RNN.ipynb @@ -1,17 +1,24 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ + "### Uczenie maszynowe\n", "# 14. Rekurencyjne sieci neuronowe" ] }, { - "attachments": {}, "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "## 14.1. Rekurencyjne sieci neuronowe" ] @@ -50,11 +57,11 @@ "source": [ "### Rekurencyjna sieć neuronowa – schemat\n", "\n", - "Rys. 11.1.\n", + "Rys. 14.1.\n", "\n", - "![Rys. 11.1. Rekurencyjna sieć neuronowa – schemat](Recurrent_neural_network_unfold.png \"Rys. 11.1. Rekurencyjna sieć neuronowa – schemat\")\n", + "![Rys. 14.1. Rekurencyjna sieć neuronowa – schemat](Recurrent_neural_network_unfold.png \"Rys. 14.1. Rekurencyjna sieć neuronowa – schemat\")\n", "\n", - "Rys. 11.1 - źródło: [fdeloche](https://commons.wikimedia.org/wiki/File:Recurrent_neural_network_unfold.svg), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" + "Rys. 14.1 - źródło: [fdeloche](https://commons.wikimedia.org/wiki/File:Recurrent_neural_network_unfold.svg), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" ] }, { @@ -67,11 +74,11 @@ "source": [ "### LSTM – schemat\n", "\n", - "Rys. 11.2.\n", + "Rys. 14.2.\n", "\n", - "![Rys. 11.2. LSTM – schemat](Long_Short-Term_Memory.png \"Rys. 11.2. LSTM – schemat\")\n", + "![Rys. 14.2. LSTM – schemat](Long_Short-Term_Memory.png \"Rys. 14.2. LSTM – schemat\")\n", "\n", - "Rys. 11.2 - źródło: [fdeloche](https://commons.wikimedia.org/wiki/File:Long_Short-Term_Memory.svg), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" + "Rys. 14.2 - źródło: [fdeloche](https://commons.wikimedia.org/wiki/File:Long_Short-Term_Memory.svg), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" ] }, { @@ -136,11 +143,11 @@ "source": [ "### GRU – schemat\n", "\n", - "Rys. 11.3\n", + "Rys. 14.3\n", "\n", - "![Rys. 11.3. GRU – schemat](Gated_Recurrent_Unit.png \"Rys. 11.3. GRU – schemat\")\n", + "![Rys. 14.3. GRU – schemat](Gated_Recurrent_Unit.png \"Rys. 14.3. GRU – schemat\")\n", "\n", - "Rys. 11.3 - źródło: [fdeloche](https://commons.wikimedia.org/wiki/File:Gated_Recurrent_Unit.svg), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" + "Rys. 14.3 - źródło: [fdeloche](https://commons.wikimedia.org/wiki/File:Gated_Recurrent_Unit.svg), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" ] }, { @@ -158,7 +165,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -197,7 +203,7 @@ "* warstwa środkowa ma $k < n$ neuronów\n", "* $y^{(i)} = x^{(i)}$ dla każdego $i$\n", "\n", - "(rys. 13.1)" + "(rys. 14.4)" ] }, { @@ -208,9 +214,9 @@ } }, "source": [ - "![Rys. 13.1. Autoencoder](Autoencoder_schema.png \"Rys. 13.1. Autoencoder\")\n", + "![Rys. 14.4. Autoencoder](Autoencoder_schema.png \"Rys. 14.4. Autoencoder\")\n", "\n", - "Rys. 13.1 - źródło: [Michela Massi](https://commons.wikimedia.org/wiki/File:Autoencoder_schema.png), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" + "Rys. 14.4 - źródło: [Michela Massi](https://commons.wikimedia.org/wiki/File:Autoencoder_schema.png), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" ] }, { @@ -240,7 +246,7 @@ "* Ograniczenia nałożone na reprezentację danych w warstwie ukrytej pozwala na „odkrycie” pewnej **struktury** w danych.\n", "* _Decoder_ musi odtworzyć do pierwotnej postaci reprezentację danych skompresowaną przez _encoder_.\n", "\n", - "(rys. 13.2)" + "(rys. 14.5)" ] }, { @@ -251,9 +257,9 @@ } }, "source": [ - "![Rys. 13.2. Autoencoder - struktura](Autoencoder_structure.png \"Rys. 13.2. Autoencoder - struktura\")\n", + "![Rys. 14.5. Autoencoder - struktura](Autoencoder_structure.png \"Rys. 14.5. Autoencoder - struktura\")\n", "\n", - "Rys. 13.2 - źródło: [Chervinskii](https://commons.wikimedia.org/wiki/File:Autoencoder_structure.png), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" + "Rys. 14.5 - źródło: [Chervinskii](https://commons.wikimedia.org/wiki/File:Autoencoder_structure.png), [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0), Wikimedia Commons" ] }, { @@ -302,7 +308,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -438,7 +443,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -481,7 +485,7 @@ "celltoolbar": "Slideshow", "email": "pawel.skorzewski@amu.edu.pl", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -496,7 +500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]" + "version": "3.10.6" }, "livereveal": { "start_slideshow_at": "selected", diff --git a/wyk/15_Uczenie_przez_wzmacnianie.ipynb b/wyk/15_Uczenie_przez_wzmacnianie.ipynb index 28735f6..5a537ee 100644 --- a/wyk/15_Uczenie_przez_wzmacnianie.ipynb +++ b/wyk/15_Uczenie_przez_wzmacnianie.ipynb @@ -2,8 +2,13 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ + "### Uczenie maszynowe\n", "# 15. Uczenie przez wzmacnianie i systemy dialogowe" ] }, @@ -15,7 +20,7 @@ } }, "source": [ - "# 15.1. Uczenie przez wzmacnianie" + "## 15.1. Uczenie przez wzmacnianie" ] }, { @@ -71,7 +76,7 @@ } }, "source": [ - "![Rys. 13.2. Agent i środowisko](agent_i_srodowisko.png \"Rys. 13.2. Agent i środowisko\")" + "![Rys. 15.2. Agent i środowisko](agent_i_srodowisko.png \"Rys. 15.2. Agent i środowisko\")" ] }, { @@ -257,9 +262,9 @@ "source": [ "## Architektura systemu dialogowego\n", "\n", - "(rys. 13.3)\n", + "(rys. 15.3)\n", "\n", - "![Rys. 13.3. Architektura systemu dialogowego](system_dialogowy.png \"Rys. 13.3. Architektura systemu dialogowego\")" + "![Rys. 15.3. Architektura systemu dialogowego](system_dialogowy.png \"Rys. 15.3. Architektura systemu dialogowego\")" ] } ], @@ -268,7 +273,7 @@ "celltoolbar": "Slideshow", "email": "pawel.skorzewski@amu.edu.pl", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -283,7 +288,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]" + "version": "3.10.6" }, "livereveal": { "start_slideshow_at": "selected", diff --git a/wyk/Autoencoder_schema.png b/wyk/Autoencoder_schema.png new file mode 100644 index 0000000000000000000000000000000000000000..b75faf5686152676f0166477c0583dacf65c7934 GIT binary patch literal 39550 zcmd432{_bi_&+?YDxs5#tSy7Ikg{Z*q|^`!r4mAA8zlQSQ%4a(&4e1uBwLa_WKXsb zW8X$(-;Lc2W4zDIAe~?5{D0Sbz1Q`BUDxSYzRz|)_wu8D*YbS!i|2EiaUOWd$tmYX4KWsKpR8xdNQUaJMhFieT+aFxI zX$ygH{6+h3Lxtcp3(k!Q|9#x42ngg zva?PT^Rw``OcET`$KAjl|Ns7VHoZOH-h%8&rW8KTm&yAsVEx&CWPFTF!BnMm%~O0Q 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