forked from pms/uczenie-maszynowe
Wykłady 13-15 - poprawki
This commit is contained in:
Paweł Skórzewski
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{
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"cells": [
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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@ -194,7 +193,7 @@
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"\n",
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"Żeby zredukować liczbę parametrów, a dzięki temu uprościć obliczenia, stosuje się warstwy ***pooling***.\n",
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"\n",
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"*Pooling* to rodzaj próbkowania. Najpopularniejszą jego odmianą jest *max-pooling*, czyli wybieranie najwyższej wartości spośród kilku sąsiadujących pikseli (rys. 12.1)."
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"*Pooling* to rodzaj próbkowania. Najpopularniejszą jego odmianą jest *max-pooling*, czyli wybieranie najwyższej wartości spośród kilku sąsiadujących pikseli (rys. 13.1)."
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@ -205,9 +204,9 @@
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}
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"source": [
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"![Rys. 12.1. Pooling](Max_pooling.png \"Rys. 12.1. Pooling\")\n",
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"![Rys. 13.1. Pooling](Max_pooling.png \"Rys. 13.1. Pooling\")\n",
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"\n",
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"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"
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"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"
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@ -218,7 +217,7 @@
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}
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},
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"source": [
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"Warstwy _pooling_ i konwolucyjne można przeplatać ze sobą (rys. 12.2)."
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"Warstwy _pooling_ i konwolucyjne można przeplatać ze sobą (rys. 13.2)."
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]
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@ -229,9 +228,9 @@
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}
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"source": [
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"![Rys. 12.2. CNN](Typical_cnn.png \"Rys. 12.2. CNN\")\n",
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"![Rys. 13.2. CNN](Typical_cnn.png \"Rys. 13.2. CNN\")\n",
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"\n",
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"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"
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"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"
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{
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@ -580,7 +579,7 @@
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"celltoolbar": "Slideshow",
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"email": "pawel.skorzewski@amu.edu.pl",
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
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"version": "3.10.6"
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},
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"start_slideshow_at": "selected",
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@ -1,17 +1,24 @@
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Uczenie maszynowe\n",
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"# 14. Rekurencyjne sieci neuronowe"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"## 14.1. Rekurencyjne sieci neuronowe"
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]
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@ -50,11 +57,11 @@
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"source": [
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"### Rekurencyjna sieć neuronowa – schemat\n",
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"\n",
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"Rys. 11.1.\n",
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"Rys. 14.1.\n",
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"\n",
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"![Rys. 11.1. Rekurencyjna sieć neuronowa – schemat](Recurrent_neural_network_unfold.png \"Rys. 11.1. Rekurencyjna sieć neuronowa – schemat\")\n",
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"![Rys. 14.1. Rekurencyjna sieć neuronowa – schemat](Recurrent_neural_network_unfold.png \"Rys. 14.1. Rekurencyjna sieć neuronowa – schemat\")\n",
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"\n",
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"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"
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"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"
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{
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@ -67,11 +74,11 @@
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"source": [
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"### LSTM – schemat\n",
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"\n",
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"Rys. 11.2.\n",
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"Rys. 14.2.\n",
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"\n",
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"![Rys. 11.2. LSTM – schemat](Long_Short-Term_Memory.png \"Rys. 11.2. LSTM – schemat\")\n",
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"![Rys. 14.2. LSTM – schemat](Long_Short-Term_Memory.png \"Rys. 14.2. LSTM – schemat\")\n",
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"\n",
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"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"
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"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"
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]
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},
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{
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"source": [
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"### GRU – schemat\n",
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"\n",
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"Rys. 11.3\n",
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"Rys. 14.3\n",
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"\n",
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"![Rys. 11.3. GRU – schemat](Gated_Recurrent_Unit.png \"Rys. 11.3. GRU – schemat\")\n",
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"![Rys. 14.3. GRU – schemat](Gated_Recurrent_Unit.png \"Rys. 14.3. GRU – schemat\")\n",
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"\n",
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"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"
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"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"
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]
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@ -158,7 +165,6 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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@ -197,7 +203,7 @@
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"* warstwa środkowa ma $k < n$ neuronów\n",
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"* $y^{(i)} = x^{(i)}$ dla każdego $i$\n",
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"\n",
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"(rys. 13.1)"
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"(rys. 14.4)"
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]
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{
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@ -208,9 +214,9 @@
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}
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},
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"source": [
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"![Rys. 13.1. Autoencoder](Autoencoder_schema.png \"Rys. 13.1. Autoencoder\")\n",
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"![Rys. 14.4. Autoencoder](Autoencoder_schema.png \"Rys. 14.4. Autoencoder\")\n",
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"\n",
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"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"
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"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"
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]
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{
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@ -240,7 +246,7 @@
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"* Ograniczenia nałożone na reprezentację danych w warstwie ukrytej pozwala na „odkrycie” pewnej **struktury** w danych.\n",
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"* _Decoder_ musi odtworzyć do pierwotnej postaci reprezentację danych skompresowaną przez _encoder_.\n",
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"\n",
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"(rys. 13.2)"
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"(rys. 14.5)"
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]
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{
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}
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},
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"source": [
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"![Rys. 13.2. Autoencoder - struktura](Autoencoder_structure.png \"Rys. 13.2. Autoencoder - struktura\")\n",
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"![Rys. 14.5. Autoencoder - struktura](Autoencoder_structure.png \"Rys. 14.5. Autoencoder - struktura\")\n",
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"\n",
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"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"
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"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"
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]
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},
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{
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@ -302,7 +308,6 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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@ -438,7 +443,6 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"celltoolbar": "Slideshow",
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"email": "pawel.skorzewski@amu.edu.pl",
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
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"version": "3.10.6"
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},
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"livereveal": {
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"start_slideshow_at": "selected",
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Uczenie maszynowe\n",
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"# 15. Uczenie przez wzmacnianie i systemy dialogowe"
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]
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},
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@ -15,7 +20,7 @@
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}
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},
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"source": [
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"# 15.1. Uczenie przez wzmacnianie"
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"## 15.1. Uczenie przez wzmacnianie"
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]
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},
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{
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}
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},
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"source": [
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"![Rys. 13.2. Agent i środowisko](agent_i_srodowisko.png \"Rys. 13.2. Agent i środowisko\")"
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"![Rys. 15.2. Agent i środowisko](agent_i_srodowisko.png \"Rys. 15.2. Agent i środowisko\")"
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]
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},
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{
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"source": [
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"## Architektura systemu dialogowego\n",
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"\n",
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"(rys. 13.3)\n",
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"(rys. 15.3)\n",
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"\n",
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"![Rys. 13.3. Architektura systemu dialogowego](system_dialogowy.png \"Rys. 13.3. Architektura systemu dialogowego\")"
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"![Rys. 15.3. Architektura systemu dialogowego](system_dialogowy.png \"Rys. 15.3. Architektura systemu dialogowego\")"
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]
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}
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],
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"celltoolbar": "Slideshow",
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"email": "pawel.skorzewski@amu.edu.pl",
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
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"version": "3.10.6"
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},
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"livereveal": {
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"start_slideshow_at": "selected",
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