Wykłady 13-15 - poprawki
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@ -194,7 +193,7 @@
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"\n",
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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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"Żeby zredukować liczbę parametrów, a dzięki temu uprościć obliczenia, stosuje się warstwy ***pooling***.\n",
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"\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)."
|
"*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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"source": [
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"source": [
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||||||
"\n",
|
"\n",
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"\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"
|
"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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}
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},
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"source": [
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"source": [
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"Warstwy _pooling_ i konwolucyjne można przeplatać ze sobą (rys. 12.2)."
|
"Warstwy _pooling_ i konwolucyjne można przeplatać ze sobą (rys. 13.2)."
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"source": [
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"source": [
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"\n",
|
"\n",
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"\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"
|
"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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"celltoolbar": "Slideshow",
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"celltoolbar": "Slideshow",
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"email": "pawel.skorzewski@amu.edu.pl",
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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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"language": "python",
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"name": "python3"
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"name": "python3"
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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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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},
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"livereveal": {
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"livereveal": {
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"start_slideshow_at": "selected",
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"start_slideshow_at": "selected",
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{
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"cells": [
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"cells": [
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"attachments": {},
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"cell_type": "markdown",
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"cell_type": "markdown",
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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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"source": [
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"### Uczenie maszynowe\n",
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"# 14. Rekurencyjne sieci neuronowe"
|
"# 14. Rekurencyjne sieci neuronowe"
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]
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{
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"attachments": {},
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"cell_type": "markdown",
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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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"source": [
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"## 14.1. Rekurencyjne sieci neuronowe"
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"## 14.1. Rekurencyjne sieci neuronowe"
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]
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]
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@ -50,11 +57,11 @@
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"source": [
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"source": [
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"### Rekurencyjna sieć neuronowa – schemat\n",
|
"### Rekurencyjna sieć neuronowa – schemat\n",
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"\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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"\n",
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"\n",
|
"\n",
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||||||
"\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"
|
"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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]
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{
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@ -67,11 +74,11 @@
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"source": [
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"source": [
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"### LSTM – schemat\n",
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"### LSTM – schemat\n",
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||||||
"\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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"\n",
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||||||
"\n",
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"\n",
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||||||
"\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"
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]
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]
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{
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{
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@ -136,11 +143,11 @@
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|||||||
"source": [
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"source": [
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||||||
"### GRU – schemat\n",
|
"### GRU – schemat\n",
|
||||||
"\n",
|
"\n",
|
||||||
"Rys. 11.3\n",
|
"Rys. 14.3\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\n",
|
"\n",
|
||||||
"\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"
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||||||
]
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]
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||||||
},
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},
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{
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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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{
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"attachments": {},
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"cell_type": "markdown",
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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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"slideshow": {
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||||||
@ -197,7 +203,7 @@
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|||||||
"* warstwa środkowa ma $k < n$ neuronów\n",
|
"* warstwa środkowa ma $k < n$ neuronów\n",
|
||||||
"* $y^{(i)} = x^{(i)}$ dla każdego $i$\n",
|
"* $y^{(i)} = x^{(i)}$ dla każdego $i$\n",
|
||||||
"\n",
|
"\n",
|
||||||
"(rys. 13.1)"
|
"(rys. 14.4)"
|
||||||
]
|
]
|
||||||
},
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},
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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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||||||
},
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},
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||||||
"source": [
|
"source": [
|
||||||
"\n",
|
"\n",
|
||||||
"\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"
|
||||||
]
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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",
|
"* 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",
|
"* _Decoder_ musi odtworzyć do pierwotnej postaci reprezentację danych skompresowaną przez _encoder_.\n",
|
||||||
"\n",
|
"\n",
|
||||||
"(rys. 13.2)"
|
"(rys. 14.5)"
|
||||||
]
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]
|
||||||
},
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},
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{
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{
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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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"source": [
|
||||||
"\n",
|
"\n",
|
||||||
"\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"
|
||||||
]
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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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||||||
"attachments": {},
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||||||
"cell_type": "markdown",
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"cell_type": "markdown",
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||||||
"metadata": {
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"metadata": {
|
||||||
"slideshow": {
|
"slideshow": {
|
||||||
@ -438,7 +443,6 @@
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|||||||
]
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]
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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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"cell_type": "markdown",
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||||||
"metadata": {
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"metadata": {
|
||||||
"slideshow": {
|
"slideshow": {
|
||||||
@ -481,7 +485,7 @@
|
|||||||
"celltoolbar": "Slideshow",
|
"celltoolbar": "Slideshow",
|
||||||
"email": "pawel.skorzewski@amu.edu.pl",
|
"email": "pawel.skorzewski@amu.edu.pl",
|
||||||
"kernelspec": {
|
"kernelspec": {
|
||||||
"display_name": "Python 3",
|
"display_name": "Python 3 (ipykernel)",
|
||||||
"language": "python",
|
"language": "python",
|
||||||
"name": "python3"
|
"name": "python3"
|
||||||
},
|
},
|
||||||
@ -496,7 +500,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
|
"version": "3.10.6"
|
||||||
},
|
},
|
||||||
"livereveal": {
|
"livereveal": {
|
||||||
"start_slideshow_at": "selected",
|
"start_slideshow_at": "selected",
|
||||||
|
@ -2,8 +2,13 @@
|
|||||||
"cells": [
|
"cells": [
|
||||||
{
|
{
|
||||||
"cell_type": "markdown",
|
"cell_type": "markdown",
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"slideshow": {
|
||||||
|
"slide_type": "slide"
|
||||||
|
}
|
||||||
|
},
|
||||||
"source": [
|
"source": [
|
||||||
|
"### Uczenie maszynowe\n",
|
||||||
"# 15. Uczenie przez wzmacnianie i systemy dialogowe"
|
"# 15. Uczenie przez wzmacnianie i systemy dialogowe"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
@ -15,7 +20,7 @@
|
|||||||
}
|
}
|
||||||
},
|
},
|
||||||
"source": [
|
"source": [
|
||||||
"# 15.1. Uczenie przez wzmacnianie"
|
"## 15.1. Uczenie przez wzmacnianie"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -71,7 +76,7 @@
|
|||||||
}
|
}
|
||||||
},
|
},
|
||||||
"source": [
|
"source": [
|
||||||
""
|
""
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -257,9 +262,9 @@
|
|||||||
"source": [
|
"source": [
|
||||||
"## Architektura systemu dialogowego\n",
|
"## Architektura systemu dialogowego\n",
|
||||||
"\n",
|
"\n",
|
||||||
"(rys. 13.3)\n",
|
"(rys. 15.3)\n",
|
||||||
"\n",
|
"\n",
|
||||||
""
|
""
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@ -268,7 +273,7 @@
|
|||||||
"celltoolbar": "Slideshow",
|
"celltoolbar": "Slideshow",
|
||||||
"email": "pawel.skorzewski@amu.edu.pl",
|
"email": "pawel.skorzewski@amu.edu.pl",
|
||||||
"kernelspec": {
|
"kernelspec": {
|
||||||
"display_name": "Python 3",
|
"display_name": "Python 3 (ipykernel)",
|
||||||
"language": "python",
|
"language": "python",
|
||||||
"name": "python3"
|
"name": "python3"
|
||||||
},
|
},
|
||||||
@ -283,7 +288,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
|
"version": "3.10.6"
|
||||||
},
|
},
|
||||||
"livereveal": {
|
"livereveal": {
|
||||||
"start_slideshow_at": "selected",
|
"start_slideshow_at": "selected",
|
||||||
|
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wyk/Autoencoder_schema.png
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wyk/Autoencoder_structure.png
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wyk/Gated_Recurrent_Unit.png
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wyk/Long_Short-Term_Memory.png
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wyk/Max_pooling.png
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wyk/Recurrent_neural_network_unfold.png
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wyk/Typical_cnn.png
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wyk/agent_i_srodowisko.png
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After Width: | Height: | Size: 39 KiB |
BIN
wyk/paradygmaty_um.png
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After Width: | Height: | Size: 63 KiB |
BIN
wyk/system_dialogowy.png
Normal file
After Width: | Height: | Size: 54 KiB |