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##
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## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore
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# ignore all pdf creation files
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package.json
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package-lock.json
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docsifytopdf.js
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docs/_sidebar.md
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# User-specific files
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*.rsuser
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*.suo
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@ -152,7 +152,7 @@ By ensuring that the content aligns with projects, the process is made more enga
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## Offline access
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You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the `etc/docsify` folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
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You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the `etc/docsify` folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`. A pdf of the curriculum is available [at this link](/etc/pdf/readme.pdf).
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## Help Wanted!
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- Introduction
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- [Introduction to AI](../../../lessons/1-Intro/README.md)
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- [Knowledge Representation and Expert Systems](../../../lessons/2-Symbolic/README.md)
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- [Neural Networks - Perceptron](../../../lessons/3-NeuralNetworks/03-Perceptron/README.md)
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- [Neural Networks - Your Own Framework](../../../lessons/3-NeuralNetworks/04-OwnFramework/README.md)
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- [Neural Networks - Perceptron](../../../lessons/3-NeuralNetworks/05-Frameworks/README.md)
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- [Computer Vision - Convolutional Neural Networks](../../../lessons/4-ComputerVision/07-ConvNets/README.md)
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- [Computer Vision - Transfer Learning](../../../lessons/4-ComputerVision/08-TransferLearning/README.md)
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- [Computer Vision - Autoencoders](../../../lessons/4-ComputerVision/09-Autoencoders/README.md)
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- [Computer Vision - GANs](../../../lessons/4-ComputerVision/10-GANs/README.md)
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- [Computer Vision - Segmentation](../../../lessons/4-ComputerVision/12-Segmentation/README.md)
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- [NLP - Text Representation](../../../lessons/5-NLP/13-TextRep/README.md)
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- [NLP - Embeddings](../../../lessons/5-NLP/14-Embeddings/README.md)
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- [NLP - LanguageModeling](../../../lessons/5-NLP/15-LanguageModeling/README.md)
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- [NLP - RNNs](../../../lessons/5-NLP/16-RNN/README.md)
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- [NLP - Generative Networks](../../../lessons/5-NLP/17-GenerativeNetworks/README.md)
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- [NLP - Transformers](../../../lessons/5-NLP/18-Transformers/README.md)
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- [Other - Genetic Algorithms](../../../lessons/6-Other/21-GeneticAlgorithms/README.md)
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- [Other - Multiagent Systems](../../../lessons/6-Other/23-MultiagentSystems/README.md)
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- [Ethics](../../../lessons/7-Ethics/README.md)
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module.exports = {
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contents: ['etc/docsify-to-pdf/docs/_sidebar.md'], // array of "table of contents" files path
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pathToPublic: 'etc/docsify-to-pdf/pdf/readme.pdf', // path where pdf will stored
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pdfOptions: {
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margin: { top: '100px', bottom: '100px' }
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}, // reference: https://github.com/GoogleChrome/puppeteer/blob/master/docs/api.md#pagepdfoptions
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removeTemp: true, // remove generated .md and .html or not
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emulateMedia: 'print', // mediaType, emulating by puppeteer for rendering pdf, 'print' by default (reference: https://github.com/GoogleChrome/puppeteer/blob/master/docs/api.md#pageemulatemediamediatype)
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};
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{
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"name": "ds-for-beginners",
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"version": "1.0.0",
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"description": "AI for Beginners - A Curriculum",
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"main": "index.js",
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"scripts": {
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"convert": "node_modules/.bin/docsify-to-pdf"
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},
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"repository": {
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"type": "git",
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"url": "git+https://github.com/microsoft/AI-For-Beginners.git"
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},
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"keywords": [
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"machine",
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"learning",
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"ml",
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"ai",
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"curriculum"
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],
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"author": "Jen Looper and team",
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"license": "MIT",
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"bugs": {
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"url": "https://github.com/microsoft/AI-For-Beginners/issues"
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},
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"homepage": "https://github.com/microsoft/AI-For-Beginners#readme",
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"devDependencies": {
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"docsify-to-pdf": "0.0.5"
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}
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}
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1. Features are processed by **Position-Sensitive Score Map**. Each object from $C$ classes is divided by $k\times k$ regions, and we are training to predict parts of objects.
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1. For each part from $k\times k$ regions all networks vote for object classes, and the object class with maximum vote is selected.
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> Image from [official paper](https://arxiv.org/abs/1605.06409)
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