650 lines
48 KiB
Plaintext
650 lines
48 KiB
Plaintext
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{
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"cells": [
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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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"## Uczenie maszynowe\n",
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"# 1. Wprowadzenie"
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]
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},
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{
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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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"## 1.1. Czym jest uczenie maszynowe?"
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]
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},
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{
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"cell_type": "markdown",
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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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"<img style=\"margin: auto\" src=\"terms-cloud.png\"/>"
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]
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},
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{
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"cell_type": "markdown",
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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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"### Sztuczna inteligencja (*artificial intelligence*)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* Naśladowanie ludzkich procesów poznawczych za pomocą komputerów"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* Konstruowanie systemów (maszyn), których działanie podobne jest do przejawów ludzkiej inteligencji"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* Dziedzina nauki, która zajmuje się naśladowaniem ludzkiej inteligencji przez komputery"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* Obejmuje m.in. logikę rozmytą, algorytmy ewolucyjne, robotykę i uczenie maszynowe"
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]
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},
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{
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"cell_type": "markdown",
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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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"<img style=\"margin: auto\" src=\"venn-ai.png\"/>"
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]
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},
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{
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"cell_type": "markdown",
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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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"### Uczenie maszynowe (*machine learning*)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* Tworzenie systemów, które potrafią doskonalić się przy pomocy zgromadzonego doświadczenia"
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]
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},
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{
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"cell_type": "markdown",
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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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"<img style=\"margin: auto\" src=\"venn-ai-ml.png\"/>"
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]
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},
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{
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"cell_type": "markdown",
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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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"### Sieci neuronowe (neural networks)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* Rodzaj struktur matematycznych, które wykonują obliczenia przy pomocy elementów zwanych _sztucznymi neuronami_"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* Budowa sieci neuronowych i zasady działania sztucznych neuronów były luźno inspirowane działaniem neuronów w mózgu"
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]
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},
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{
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"cell_type": "markdown",
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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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"<img style=\"margin: auto\" src=\"venn-ai-ml-nn.png\"/>"
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]
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},
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{
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"cell_type": "markdown",
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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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"### Uczenie głębokie (deep learning)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* Użycie sieci neuronowych do automatycznego wydobywania cech z surowych danych"
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]
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},
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{
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"cell_type": "markdown",
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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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"<img style=\"margin: auto\" src=\"venn-ai-ml-nn-dl.png\"/>"
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]
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},
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{
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"cell_type": "markdown",
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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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"### Data science"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* Dziedzina nauki zajmująca się przetwarzaniem danych w celu wydobycia z nich wiedzy"
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]
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},
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{
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"cell_type": "markdown",
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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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"<img style=\"margin: auto\" src=\"venn-ai-ml-nn-dl-ds.png\"/>"
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]
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},
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{
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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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"### Uczenie maszynowe – klasyczne definicje"
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]
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},
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{
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"cell_type": "markdown",
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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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"<img style=\"float: right;\" src=\"https://upload.wikimedia.org/wikipedia/commons/f/f8/This_is_the_photo_of_Arthur_Samuel.jpg\"/>\n",
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"\n",
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"> Uczenie maszynowe to dziedzina nauki,\n",
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"> która daje komputerom umiejętność uczenia się\n",
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"> bez programowania ich _explicite_.\n",
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"\n",
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"> — <cite>Arthur Samuel, 1959</cite>"
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]
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},
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{
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"cell_type": "markdown",
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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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"<img style=\"float: right;\" width=\"20%\" src=\"http://mediad.publicbroadcasting.net/p/wamc/files/styles/x_large/public/201401/tom_mitchell.jpg\"/>\n",
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"\n",
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"> Mówimy, że program komputerowy **uczy się**\n",
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"> z doświadczenia E w odniesieniu do zadania T i miary skuteczności P,\n",
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"> jeżeli jego skuteczność wykonywania zadania T mierzona według P\n",
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"> wzrasta z doświadczeniem E.\n",
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"\n",
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"> — <cite>Tom Mitchell, 1998</cite>"
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]
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},
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{
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"cell_type": "markdown",
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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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"<img style=\"margin: auto\" width=\"40%\" src=\"https://static1.squarespace.com/static/5150aec6e4b0e340ec52710a/t/51525c33e4b0b3e0d10f77ab/1364352052403/Data_Science_VD.png\"/>\n",
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"\n",
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"<sub>Źródło: Drew Conway, http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram</sub>"
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]
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},
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{
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"cell_type": "markdown",
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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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"Uczenie maszynowe to:\n",
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"\n",
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"* doskonalenie działania dla pewnych zadań na podstawie doświadczenia"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* tworzenie systemów, które doskonalą swoje działania na podstawie przeszłych doświadczeń"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"* zestaw metod, które potrafią w sposób automatyczny wykrywać wzorce w danych, a następnie używać wcześniej niezaobserwowanych wzorców do przewidywania przyszłych zjawisk"
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]
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},
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{
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"cell_type": "markdown",
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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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"Charakterystyczne cechy uczenia maszynowego:\n",
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"\n",
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"* „automatyzacja automatyzacji”\n",
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"* komputer „sam się programuje”\n",
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"* modelowanie danych zastępuje pisanie programu"
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]
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},
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{
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"cell_type": "markdown",
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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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"<img style=\"margin: auto\" width=\"80%\" src=\"https://recast.ai/blog/wp-content/uploads/2017/02/image20.png\"/>\n",
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"\n",
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"<sub>Źródło: https://recast.ai/blog/machine-learning-algorithms/</sub>"
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|||
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]
|
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},
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|||
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{
|
|||
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"cell_type": "code",
|
|||
|
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"execution_count": 4,
|
|||
|
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"metadata": {
|
|||
|
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"slideshow": {
|
|||
|
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"slide_type": "notes"
|
|||
|
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}
|
|||
|
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},
|
|||
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"outputs": [],
|
|||
|
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"source": [
|
|||
|
|
"import IPython"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 5,
|
|||
|
|
"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/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",
|
|||
|
|
"> — 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
|
|||
|
|
}
|