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GitHub - trekhleb/machine-learning-experiments: 🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
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data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a 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</div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button 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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" 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dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🤖 Interactive Machine Learning Experiments\u003c/h1\u003e\u003ca id=\"user-content--interactive-machine-learning-experiments\" class=\"anchor\" aria-label=\"Permalink: 🤖 Interactive Machine Learning Experiments\" href=\"#-interactive-machine-learning-experiments\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e🇺🇦 UKRAINE \u003ca href=\"https://war.ukraine.ua/\" rel=\"nofollow\"\u003eIS BEING ATTACKED\u003c/a\u003e BY RUSSIAN ARMY. CIVILIANS ARE GETTING KILLED. RESIDENTIAL AREAS ARE GETTING BOMBED.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eHelp Ukraine via:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://prytulafoundation.org/en/\" rel=\"nofollow\"\u003eSerhiy Prytula Charity Foundation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://savelife.in.ua/en/donate-en/\" rel=\"nofollow\"\u003eCome Back Alive Charity Foundation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://bank.gov.ua/en/news/all/natsionalniy-bank-vidkriv-spetsrahunok-dlya-zboru-koshtiv-na-potrebi-armiyi\" rel=\"nofollow\"\u003eNational Bank of Ukraine\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eMore info on \u003ca href=\"https://war.ukraine.ua/\" rel=\"nofollow\"\u003ewar.ukraine.ua\u003c/a\u003e and \u003ca href=\"https://twitter.com/MFA_Ukraine\" rel=\"nofollow\"\u003eMFA of Ukraine\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003eThis is a collection of interactive machine-learning experiments. Each experiment consists of 🏋️ Jupyter/Colab \u003cem\u003enotebook\u003c/em\u003e (to see how a model was trained) and 🎨 \u003cem\u003edemo page\u003c/em\u003e (to see a model in action right in your browser).\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e🎨 \u003ca href=\"http://trekhleb.github.io/machine-learning-experiments/\" rel=\"nofollow\"\u003eLaunch ML experiments demo\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e🏋️ \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/tree/master/experiments/\" rel=\"nofollow\"\u003eLaunch ML experiments Jupyter notebooks\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eYou might also be interested in \u003ca href=\"https://github.com/trekhleb/homemade-gpt-js\"\u003eHomemade GPT • JS\u003c/a\u003e\u003c/em\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e⚠️\u003c/g-emoji\u003e This repository contains machine learning \u003cstrong\u003eexperiments\u003c/strong\u003e and \u003cstrong\u003enot\u003c/strong\u003e a production ready, reusable, optimised and fine-tuned code and models. This is rather a sandbox or a playground for learning and trying different machine learning approaches, algorithms and data-sets. Models might not perform well and there is a place for overfitting/underfitting.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExperiments\u003c/h2\u003e\u003ca id=\"user-content-experiments\" class=\"anchor\" aria-label=\"Permalink: Experiments\" href=\"#experiments\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eMost of the models in these experiments were trained using \u003ca href=\"https://www.tensorflow.org/\" rel=\"nofollow\"\u003eTensorFlow 2\u003c/a\u003e with \u003ca href=\"https://www.tensorflow.org/guide/keras/overview\" rel=\"nofollow\"\u003eKeras\u003c/a\u003e support.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSupervised Machine Learning\u003c/h3\u003e\u003ca id=\"user-content-supervised-machine-learning\" class=\"anchor\" aria-label=\"Permalink: Supervised Machine Learning\" href=\"#supervised-machine-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://en.wikipedia.org/wiki/Supervised_learning\" rel=\"nofollow\"\u003eSupervised learning\u003c/a\u003e is when you have input variables \u003ccode\u003eX\u003c/code\u003e and an output variable \u003ccode\u003eY\u003c/code\u003e and you use an algorithm to learn the mapping function from the input to the output: \u003ccode\u003eY = f(X)\u003c/code\u003e. The goal is to approximate the mapping function so well that when you have new input data \u003ccode\u003eX\u003c/code\u003e that you can predict the output variables \u003ccode\u003eY\u003c/code\u003e for that data. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMultilayer Perceptron (MLP) or simple Neural Network (NN)\u003c/h4\u003e\u003ca id=\"user-content-multilayer-perceptron-mlp-or-simple-neural-network-nn\" class=\"anchor\" aria-label=\"Permalink: Multilayer Perceptron (MLP) or simple Neural Network (NN)\" href=\"#multilayer-perceptron-mlp-or-simple-neural-network-nn\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA \u003ca href=\"https://en.wikipedia.org/wiki/Multilayer_perceptron\" rel=\"nofollow\"\u003emultilayer perceptron\u003c/a\u003e (MLP) is a class of feedforward artificial neural network (ANN). Multilayer perceptrons are sometimes referred to as \"vanilla\" neural networks (composed of multiple layers of perceptrons), especially when they have a single hidden layer. It can distinguish data that is not linearly separable.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" width=\"150\"\u003e \u003c/th\u003e\n \u003cth align=\"left\" width=\"200\"\u003eExperiment\u003c/th\u003e\n \u003cth align=\"left\" width=\"140\"\u003eModel demo \u0026amp; training\u003c/th\u003e\n \u003cth align=\"left\"\u003eTags\u003c/th\u003e\n \u003cth align=\"left\" width=\"140\"\u003eDataset\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/digits_recognition_mlp.png\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/digits_recognition_mlp.png\" alt=\"Handwritten digits recognition (MLP)\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_mlp/digits_recognition_mlp.ipynb\"\u003e\n \u003cb\u003eHandwritten Digits Recognition (MLP)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/DigitsRecognitionMLP\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_mlp/digits_recognition_mlp.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_mlp/digits_recognition_mlp.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eMLP\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://www.tensorflow.org/datasets/catalog/mnist\" rel=\"nofollow\"\u003e\n MNIST\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/sketch_recognition_mlp.png\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/sketch_recognition_mlp.png\" alt=\"Handwritten sketch recognition (MLP)\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_mlp/sketch_recognition_mlp.ipynb\"\u003e\n \u003cb\u003eHandwritten Sketch Recognition (MLP)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/SketchRecognitionMLP\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_mlp/sketch_recognition_mlp.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_mlp/sketch_recognition_mlp.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eMLP\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://github.com/googlecreativelab/quickdraw-dataset\"\u003e\n QuickDraw\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConvolutional Neural Networks (CNN)\u003c/h4\u003e\u003ca id=\"user-content-convolutional-neural-networks-cnn\" class=\"anchor\" aria-label=\"Permalink: Convolutional Neural Networks (CNN)\" href=\"#convolutional-neural-networks-cnn\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA \u003ca href=\"https://en.wikipedia.org/wiki/Convolutional_neural_network\" rel=\"nofollow\"\u003econvolutional neural network\u003c/a\u003e (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery (photos, videos). They are used for detecting and classifying objects on photos and videos, style transfer, face recognition, pose estimation etc.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" width=\"150\"\u003e \u003c/th\u003e\n \u003cth align=\"left\" width=\"200\"\u003eExperiment\u003c/th\u003e\n \u003cth align=\"left\" width=\"140\"\u003eModel demo \u0026amp; training\u003c/th\u003e\n \u003cth align=\"left\"\u003eTags\u003c/th\u003e\n \u003cth align=\"left\" width=\"140\"\u003eDataset\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/digits_recognition_cnn.png\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/digits_recognition_cnn.png\" alt=\"Handwritten digits recognition (CNN)\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_cnn/digits_recognition_cnn.ipynb\"\u003e\n \u003cb\u003eHandwritten Digits Recognition (CNN)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/DigitsRecognitionCNN\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_cnn/digits_recognition_cnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_cnn/digits_recognition_cnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eCNN\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://www.tensorflow.org/datasets/catalog/mnist\" rel=\"nofollow\"\u003e\n MNIST\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/sketch_recognition_cnn.png\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/sketch_recognition_cnn.png\" alt=\"Handwritten sketch recognition (CNN)\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_cnn/sketch_recognition_cnn.ipynb\"\u003e\n \u003cb\u003eHandwritten Sketch Recognition (CNN)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/SketchRecognitionCNN\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_cnn/sketch_recognition_cnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_cnn/sketch_recognition_cnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eCNN\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://github.com/googlecreativelab/quickdraw-dataset\"\u003e\n QuickDraw\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/rock_paper_scissors_cnn.jpg\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/rock_paper_scissors_cnn.jpg\" alt=\"Rock Paper Scissors\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_cnn/rock_paper_scissors_cnn.ipynb\"\u003e\n \u003cb\u003eRock Paper Scissors (CNN)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/RockPaperScissorsCNN\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_cnn/rock_paper_scissors_cnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_cnn/rock_paper_scissors_cnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eCNN\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"http://www.laurencemoroney.com/rock-paper-scissors-dataset/\" rel=\"nofollow\"\u003e\n RPS\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/rock_paper_scissors_mobilenet_v2.jpg\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/rock_paper_scissors_mobilenet_v2.jpg\" alt=\"Rock Paper Scissors\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_mobilenet_v2/rock_paper_scissors_mobilenet_v2.ipynb\"\u003e\n \u003cb\u003eRock Paper Scissors (MobilenetV2)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/RockPaperScissorsMobilenetV2\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_mobilenet_v2/rock_paper_scissors_mobilenet_v2.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_mobilenet_v2/rock_paper_scissors_mobilenet_v2.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eMobileNetV2\u003c/code\u003e,\n \u003ccode\u003eTransfer learning\u003c/code\u003e,\n \u003ccode\u003eCNN\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"http://www.laurencemoroney.com/rock-paper-scissors-dataset/\" rel=\"nofollow\"\u003e\n RPS\n \u003c/a\u003e,\n \u003ca href=\"http://image-net.org/explore\" rel=\"nofollow\"\u003e\n ImageNet\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/objects_detection_ssdlite_mobilenet_v2.jpg\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/objects_detection_ssdlite_mobilenet_v2.jpg\" alt=\"Objects detection\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/objects_detection_ssdlite_mobilenet_v2/objects_detection_ssdlite_mobilenet_v2.ipynb\"\u003e\n \u003cb\u003eObjects Detection (MobileNetV2)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/ObjectsDetectionSSDLiteMobilenetV2\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/objects_detection_ssdlite_mobilenet_v2/objects_detection_ssdlite_mobilenet_v2.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/objects_detection_ssdlite_mobilenet_v2/objects_detection_ssdlite_mobilenet_v2.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eMobileNetV2\u003c/code\u003e,\n \u003ccode\u003eSSDLite\u003c/code\u003e,\n \u003ccode\u003eCNN\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"http://cocodataset.org/#home\" rel=\"nofollow\"\u003e\n COCO\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/image_classification_mobilenet_v2.jpg\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/image_classification_mobilenet_v2.jpg\" alt=\"Objects detection\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/image_classification_mobilenet_v2/image_classification_mobilenet_v2.ipynb\"\u003e\n \u003cb\u003eImage Classification (MobileNetV2)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/ImageClassificationMobilenetV2\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/image_classification_mobilenet_v2/image_classification_mobilenet_v2.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/image_classification_mobilenet_v2/image_classification_mobilenet_v2.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eMobileNetV2\u003c/code\u003e,\n \u003ccode\u003eCNN\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"http://image-net.org/explore\" rel=\"nofollow\"\u003e\n ImageNet\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRecurrent Neural Networks (RNN)\u003c/h4\u003e\u003ca id=\"user-content-recurrent-neural-networks-rnn\" class=\"anchor\" aria-label=\"Permalink: Recurrent Neural Networks (RNN)\" href=\"#recurrent-neural-networks-rnn\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA \u003ca href=\"https://en.wikipedia.org/wiki/Recurrent_neural_network\" rel=\"nofollow\"\u003erecurrent neural network\u003c/a\u003e (RNN) is a class of deep neural networks, most commonly applied to sequence-based data like speech, voice, text or music. They are used for machine translation, speech recognition, voice synthesis etc.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" width=\"150\"\u003e \u003c/th\u003e\n \u003cth align=\"left\" width=\"200\"\u003eExperiment\u003c/th\u003e\n \u003cth align=\"left\" width=\"140\"\u003eModel demo \u0026amp; training\u003c/th\u003e\n \u003cth align=\"left\"\u003eTags\u003c/th\u003e\n \u003cth align=\"left\" width=\"140\"\u003eDataset\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/numbers_summation_rnn.png\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/numbers_summation_rnn.png\" alt=\"Numbers summation (RNN)\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/numbers_summation_rnn/numbers_summation_rnn.ipynb\"\u003e\n \u003cb\u003eNumbers Summation (RNN)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/NumbersSummationRNN\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/numbers_summation_rnn/numbers_summation_rnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/numbers_summation_rnn/numbers_summation_rnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eLSTM\u003c/code\u003e,\n \u003ccode\u003eSequence-to-sequence\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n Auto-generated\n \u003c/td\u003e\n \u003c/tr\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/text_generation_shakespeare_rnn.jpg\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/text_generation_shakespeare_rnn.jpg\" alt=\"Shakespeare Text Generation (RNN)\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_shakespeare_rnn/text_generation_shakespeare_rnn.ipynb\"\u003e\n \u003cb\u003eShakespeare Text Generation (RNN)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/TextGenerationShakespeareRNN\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_shakespeare_rnn/text_generation_shakespeare_rnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_shakespeare_rnn/text_generation_shakespeare_rnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eLSTM\u003c/code\u003e,\n \u003ccode\u003eCharacter-based RNN\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt\" rel=\"nofollow\"\u003e\n Shakespeare\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/text_generation_wikipedia_rnn.png\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/text_generation_wikipedia_rnn.png\" alt=\"Wikipedia Text Generation (RNN)\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_wikipedia_rnn/text_generation_wikipedia_rnn.ipynb\"\u003e\n \u003cb\u003eWikipedia Text Generation (RNN)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/TextGenerationWikipediaRNN\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_wikipedia_rnn/text_generation_wikipedia_rnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_wikipedia_rnn/text_generation_wikipedia_rnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eLSTM\u003c/code\u003e,\n \u003ccode\u003eCharacter-based RNN\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://www.tensorflow.org/datasets/catalog/wikipedia\" rel=\"nofollow\"\u003e\n Wikipedia\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/recipe_generation_rnn.jpg\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/recipe_generation_rnn.jpg\" alt=\"Recipe Generation (RNN)\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/recipe_generation_rnn/recipe_generation_rnn.ipynb\"\u003e\n \u003cb\u003eRecipe Generation (RNN)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/RecipeGenerationRNN\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/recipe_generation_rnn/recipe_generation_rnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/recipe_generation_rnn/recipe_generation_rnn.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eLSTM\u003c/code\u003e,\n \u003ccode\u003eCharacter-based RNN\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://eightportions.com/datasets/Recipes/\" rel=\"nofollow\"\u003e\n Recipe box\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUnsupervised Machine Learning\u003c/h3\u003e\u003ca id=\"user-content-unsupervised-machine-learning\" class=\"anchor\" aria-label=\"Permalink: Unsupervised Machine Learning\" href=\"#unsupervised-machine-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://en.wikipedia.org/wiki/Unsupervised_learning\" rel=\"nofollow\"\u003eUnsupervised learning\u003c/a\u003e is when you only have input data \u003ccode\u003eX\u003c/code\u003e and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own to discover and present the interesting structure in the data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGenerative Adversarial Networks (GANs)\u003c/h4\u003e\u003ca id=\"user-content-generative-adversarial-networks-gans\" class=\"anchor\" aria-label=\"Permalink: Generative Adversarial Networks (GANs)\" href=\"#generative-adversarial-networks-gans\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA \u003ca href=\"https://en.wikipedia.org/wiki/Generative_adversarial_network\" rel=\"nofollow\"\u003egenerative adversarial network\u003c/a\u003e (GAN) is a class of machine learning frameworks where two neural networks contest with each other in a game. Two models are trained simultaneously by an adversarial process. For example a \u003cem\u003egenerator\u003c/em\u003e (\"the artist\") learns to create images that look real, while a \u003cem\u003ediscriminator\u003c/em\u003e (\"the art critic\") learns to tell real images apart from fakes.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" width=\"150\"\u003e \u003c/th\u003e\n \u003cth align=\"left\" width=\"200\"\u003eExperiment\u003c/th\u003e\n \u003cth align=\"left\" width=\"140\"\u003eModel demo \u0026amp; training\u003c/th\u003e\n \u003cth align=\"left\"\u003eTags\u003c/th\u003e\n \u003cth align=\"left\" width=\"140\"\u003eDataset\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/trekhleb/machine-learning-experiments/blob/master/demos/src/images/clothes_generation_dcgan.jpg\"\u003e\u003cimg src=\"/trekhleb/machine-learning-experiments/raw/master/demos/src/images/clothes_generation_dcgan.jpg\" alt=\"Clothes Generation (DCGAN)\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"/trekhleb/machine-learning-experiments/blob/master/experiments/clothes_generation_dcgan/clothes_generation_dcgan.ipynb\"\u003e\n \u003cb\u003eClothes Generation (DCGAN)\u003c/b\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://trekhleb.github.io/machine-learning-experiments/#/experiments/ClothesGenerationDCGAN\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e\" alt=\"Launch demo\" data-canonical-src=\"https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch\u0026amp;message=Demo\u0026amp;color=green\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/clothes_generation_dcgan/clothes_generation_dcgan.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667\" alt=\"Open in Binder\" data-canonical-src=\"https://mybinder.org/badge_logo.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/clothes_generation_dcgan/clothes_generation_dcgan.ipynb\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667\" alt=\"Open in Colab\" data-canonical-src=\"https://colab.research.google.com/assets/colab-badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ccode\u003eDCGAN\u003c/code\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://www.tensorflow.org/datasets/catalog/fashion_mnist\" rel=\"nofollow\"\u003e\n Fashion MNIST\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to use this repository locally\u003c/h2\u003e\u003ca id=\"user-content-how-to-use-this-repository-locally\" class=\"anchor\" aria-label=\"Permalink: How to use this repository locally\" href=\"#how-to-use-this-repository-locally\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSetup virtual environment for Experiments\u003c/h3\u003e\u003ca id=\"user-content-setup-virtual-environment-for-experiments\" class=\"anchor\" aria-label=\"Permalink: Setup virtual environment for Experiments\" href=\"#setup-virtual-environment-for-experiments\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Create \u0026quot;experiments\u0026quot; environment (from the project root folder).\npython3 -m venv .virtualenvs/experiments\n\n# Activate environment.\nsource .virtualenvs/experiments/bin/activate\n# or if you use Fish...\nsource .virtualenvs/experiments/bin/activate.fish\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create \"experiments\" environment (from the project root folder).\u003c/span\u003e\npython3 -m venv .virtualenvs/experiments\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Activate environment.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e .virtualenvs/experiments/bin/activate\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or if you use Fish...\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e .virtualenvs/experiments/bin/activate.fish\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo quit an environment run \u003ccode\u003edeactivate\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall dependencies\u003c/h3\u003e\u003ca id=\"user-content-install-dependencies\" class=\"anchor\" aria-label=\"Permalink: Install dependencies\" href=\"#install-dependencies\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Upgrade pip and setuptools to the latest versions.\npip install --upgrade pip setuptools\n\n# Install packages\npip install -r requirements.txt\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Upgrade pip and setuptools to the latest versions.\u003c/span\u003e\npip install --upgrade pip setuptools\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install packages\u003c/span\u003e\npip install -r requirements.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install new packages run \u003ccode\u003epip install package-name\u003c/code\u003e. To add new packages to the requirements run \u003ccode\u003epip freeze \u0026gt; requirements.txt\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLaunch Jupyter locally\u003c/h3\u003e\u003ca id=\"user-content-launch-jupyter-locally\" class=\"anchor\" aria-label=\"Permalink: Launch Jupyter locally\" href=\"#launch-jupyter-locally\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn order to play around with Jupyter notebooks and see how models were trained you need to launch a \u003ca href=\"https://jupyter.org/\" rel=\"nofollow\"\u003eJupyter Notebook\u003c/a\u003e server.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Launch Jupyter server.\njupyter notebook\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Launch Jupyter server.\u003c/span\u003e\njupyter notebook\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eJupyter will be available locally at \u003ccode\u003ehttp://localhost:8888/\u003c/code\u003e. Notebooks with experiments may be found in \u003ccode\u003eexperiments\u003c/code\u003e folder.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLaunch demos locally\u003c/h3\u003e\u003ca id=\"user-content-launch-demos-locally\" class=\"anchor\" aria-label=\"Permalink: Launch demos locally\" href=\"#launch-demos-locally\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDemo application is made on React by means of \u003ca href=\"https://github.com/facebook/create-react-app\"\u003ecreate-react-app\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Switch to demos folder from project root.\ncd demos\n\n# Install all dependencies.\nyarn install\n\n# Start demo server on http. \nyarn start\n\n# Or start demo server on https (for camera access in browser to work on localhost).\nyarn start-https\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Switch to demos folder from project root.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e demos\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install all dependencies.\u003c/span\u003e\nyarn install\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Start demo server on http. \u003c/span\u003e\nyarn start\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Or start demo server on https (for camera access in browser to work on localhost).\u003c/span\u003e\nyarn start-https\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDemos will be available locally at \u003ccode\u003ehttp://localhost:3000/\u003c/code\u003e or at \u003ccode\u003ehttps://localhost:3000/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConvert models\u003c/h3\u003e\u003ca id=\"user-content-convert-models\" class=\"anchor\" aria-label=\"Permalink: Convert models\" href=\"#convert-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ccode\u003econverter\u003c/code\u003e environment is used to convert the models that were trained during the experiments from \u003ccode\u003e.h5\u003c/code\u003e Keras format to Javascript understandable formats (\u003ccode\u003etfjs_layers_model\u003c/code\u003e or \u003ccode\u003etfjs_graph_model\u003c/code\u003e formats with \u003ccode\u003e.json\u003c/code\u003e and \u003ccode\u003e.bin\u003c/code\u003e files) for further usage with \u003ca href=\"https://www.tensorflow.org/js\" rel=\"nofollow\"\u003eTensorFlow.js\u003c/a\u003e in \u003ca href=\"http://trekhleb.github.io/machine-learning-experiments/\" rel=\"nofollow\"\u003eDemo application\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Create \u0026quot;converter\u0026quot; environment (from the project root folder).\npython3 -m venv .virtualenvs/converter\n\n# Activate \u0026quot;converter\u0026quot; environment.\nsource .virtualenvs/converter/bin/activate\n# or if you use Fish...\nsource .virtualenvs/converter/bin/activate.fish\n\n# Install converter requirements.\npip install -r requirements.converter.txt\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Create \"converter\" environment (from the project root folder).\u003c/span\u003e\npython3 -m venv .virtualenvs/converter\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Activate \"converter\" environment.\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e .virtualenvs/converter/bin/activate\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or if you use Fish...\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e .virtualenvs/converter/bin/activate.fish\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Install converter requirements.\u003c/span\u003e\npip install -r requirements.converter.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe conversion of \u003ccode\u003ekeras\u003c/code\u003e models to \u003ccode\u003etfjs_layers_model\u003c/code\u003e/\u003ccode\u003etfjs_graph_model\u003c/code\u003e formats is done by \u003ca href=\"https://github.com/tensorflow/tfjs/tree/master/tfjs-converter\"\u003etfjs-converter\u003c/a\u003e:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"tensorflowjs_converter --input_format keras \\\n ./experiments/digits_recognition_mlp/digits_recognition_mlp.h5 \\\n ./demos/public/models/digits_recognition_mlp\"\u003e\u003cpre\u003etensorflowjs_converter --input_format keras \\\n ./experiments/digits_recognition_mlp/digits_recognition_mlp.h5 \\\n ./demos/public/models/digits_recognition_mlp\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e⚠️\u003c/g-emoji\u003e Converting the models to JS understandable formats and loading them to the browser directly might not be a good practice since in this case the user might need to load tens or hundreds of megabytes of data to the browser which is not efficient. Normally the model is being served from the back-end (i.e. \u003ca href=\"https://www.tensorflow.org/tfx\" rel=\"nofollow\"\u003eTensorFlow Extended\u003c/a\u003e) and instead of loading it all to the browser the user will do a lightweight HTTP request to do a prediction. But since the \u003ca href=\"http://trekhleb.github.io/machine-learning-experiments/\" rel=\"nofollow\"\u003eDemo App\u003c/a\u003e is just an experiment and not a production-ready app and for the sake of simplicity (to avoid having an up and running back-end) we're converting the models to JS understandable formats and loading them directly into the browser.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRequirements\u003c/h3\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eRecommended versions:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePython: \u003ccode\u003e\u0026gt; 3.7.3\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eNode: \u003ccode\u003e\u0026gt;= 12.4.0\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eYarn: \u003ccode\u003e\u0026gt;= 1.13.0\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eIn case if you have Python version \u003ccode\u003e3.7.3\u003c/code\u003e you might experience \u003ccode\u003eRuntimeError: dictionary changed size during iteration\u003c/code\u003e error when trying to \u003ccode\u003eimport tensorflow\u003c/code\u003e (see the \u003ca href=\"https://github.com/tensorflow/tensorflow/issues/33183\" data-hovercard-type=\"issue\" data-hovercard-url=\"/tensorflow/tensorflow/issues/33183/hovercard\"\u003eissue\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eYou might also be interested in\u003c/h2\u003e\u003ca id=\"user-content-you-might-also-be-interested-in\" class=\"anchor\" aria-label=\"Permalink: You might also be interested in\" href=\"#you-might-also-be-interested-in\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/trekhleb/homemade-machine-learning/\"\u003eHomemade Machine Learning\u003c/a\u003e - Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/trekhleb/nano-neuron\"\u003eNanoNeuron\u003c/a\u003e - 7 simple JavaScript functions that will give you a feeling of how machines can actually \"learn\".\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/trekhleb/learn-python\"\u003ePlayground and Cheatsheet for Learning Python\u003c/a\u003e - Collection of Python scripts that are split by topics and contain code examples with explanations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eArticles\u003c/h2\u003e\u003ca id=\"user-content-articles\" class=\"anchor\" aria-label=\"Permalink: Articles\" href=\"#articles\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e📝 \u003ca href=\"https://github.com/trekhleb/machine-learning-experiments/blob/master/assets/story.en.md\"\u003eStory behind the project\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e📝 \u003ca href=\"https://github.com/trekhleb/machine-learning-experiments/blob/master/assets/recipes_generation.en.md\"\u003eGenerating cooking recipes using TensorFlow and LSTM Recurrent Neural Network\u003c/a\u003e (a step-by-step guide)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAuthor\u003c/h2\u003e\u003ca id=\"user-content-author\" class=\"anchor\" aria-label=\"Permalink: Author\" href=\"#author\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://trekhleb.dev\" rel=\"nofollow\"\u003e@trekhleb\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"🤖 Interactive Machine Learning Experiments","anchor":"-interactive-machine-learning-experiments","htmlText":"🤖 Interactive Machine Learning Experiments"},{"level":2,"text":"Experiments","anchor":"experiments","htmlText":"Experiments"},{"level":3,"text":"Supervised Machine Learning","anchor":"supervised-machine-learning","htmlText":"Supervised Machine Learning"},{"level":4,"text":"Multilayer Perceptron (MLP) or simple Neural Network (NN)","anchor":"multilayer-perceptron-mlp-or-simple-neural-network-nn","htmlText":"Multilayer Perceptron (MLP) or simple Neural Network (NN)"},{"level":4,"text":"Convolutional Neural Networks (CNN)","anchor":"convolutional-neural-networks-cnn","htmlText":"Convolutional Neural Networks (CNN)"},{"level":4,"text":"Recurrent Neural Networks (RNN)","anchor":"recurrent-neural-networks-rnn","htmlText":"Recurrent Neural Networks (RNN)"},{"level":3,"text":"Unsupervised Machine Learning","anchor":"unsupervised-machine-learning","htmlText":"Unsupervised Machine Learning"},{"level":4,"text":"Generative Adversarial Networks (GANs)","anchor":"generative-adversarial-networks-gans","htmlText":"Generative Adversarial Networks (GANs)"},{"level":2,"text":"How to use this repository locally","anchor":"how-to-use-this-repository-locally","htmlText":"How to use this repository locally"},{"level":3,"text":"Setup virtual environment for Experiments","anchor":"setup-virtual-environment-for-experiments","htmlText":"Setup virtual environment for Experiments"},{"level":3,"text":"Install dependencies","anchor":"install-dependencies","htmlText":"Install dependencies"},{"level":3,"text":"Launch Jupyter locally","anchor":"launch-jupyter-locally","htmlText":"Launch Jupyter locally"},{"level":3,"text":"Launch demos locally","anchor":"launch-demos-locally","htmlText":"Launch demos locally"},{"level":3,"text":"Convert models","anchor":"convert-models","htmlText":"Convert models"},{"level":3,"text":"Requirements","anchor":"requirements","htmlText":"Requirements"},{"level":2,"text":"You might also be interested in","anchor":"you-might-also-be-interested-in","htmlText":"You might also be interested in"},{"level":2,"text":"Articles","anchor":"articles","htmlText":"Articles"},{"level":2,"text":"Author","anchor":"author","htmlText":"Author"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Ftrekhleb%2Fmachine-learning-experiments"}},{"displayName":"LICENSE","repoName":"machine-learning-experiments","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"MIT","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Ftrekhleb%2Fmachine-learning-experiments"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_smell_icebreaker_ux":true,"accessible_code_button":true}}}}</script> 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dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">🤖 Interactive Machine Learning Experiments</h1><a id="user-content--interactive-machine-learning-experiments" class="anchor" aria-label="Permalink: 🤖 Interactive Machine Learning Experiments" href="#-interactive-machine-learning-experiments"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <blockquote> <p dir="auto">🇺🇦 UKRAINE <a href="https://war.ukraine.ua/" rel="nofollow">IS BEING ATTACKED</a> BY RUSSIAN ARMY. CIVILIANS ARE GETTING KILLED. RESIDENTIAL AREAS ARE GETTING BOMBED.</p> <ul dir="auto"> <li>Help Ukraine via: <ul dir="auto"> <li><a href="https://prytulafoundation.org/en/" rel="nofollow">Serhiy Prytula Charity Foundation</a></li> <li><a href="https://savelife.in.ua/en/donate-en/" rel="nofollow">Come Back Alive Charity Foundation</a></li> <li><a href="https://bank.gov.ua/en/news/all/natsionalniy-bank-vidkriv-spetsrahunok-dlya-zboru-koshtiv-na-potrebi-armiyi" rel="nofollow">National Bank of Ukraine</a></li> </ul> </li> <li>More info on <a href="https://war.ukraine.ua/" rel="nofollow">war.ukraine.ua</a> and <a href="https://twitter.com/MFA_Ukraine" rel="nofollow">MFA of Ukraine</a></li> </ul> </blockquote> <hr> <p dir="auto">This is a collection of interactive machine-learning experiments. Each experiment consists of 🏋️ Jupyter/Colab <em>notebook</em> (to see how a model was trained) and 🎨 <em>demo page</em> (to see a model in action right in your browser).</p> <ul dir="auto"> <li>🎨 <a href="http://trekhleb.github.io/machine-learning-experiments/" rel="nofollow">Launch ML experiments demo</a></li> <li>🏋️ <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/tree/master/experiments/" rel="nofollow">Launch ML experiments Jupyter notebooks</a></li> </ul> <blockquote> <p dir="auto"><em>You might also be interested in <a href="https://github.com/trekhleb/homemade-gpt-js">Homemade GPT • JS</a></em></p> </blockquote> <blockquote> <p dir="auto"><g-emoji class="g-emoji" alias="warning">⚠️</g-emoji> This repository contains machine learning <strong>experiments</strong> and <strong>not</strong> a production ready, reusable, optimised and fine-tuned code and models. This is rather a sandbox or a playground for learning and trying different machine learning approaches, algorithms and data-sets. Models might not perform well and there is a place for overfitting/underfitting.</p> </blockquote> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Experiments</h2><a id="user-content-experiments" class="anchor" aria-label="Permalink: Experiments" href="#experiments"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Most of the models in these experiments were trained using <a href="https://www.tensorflow.org/" rel="nofollow">TensorFlow 2</a> with <a href="https://www.tensorflow.org/guide/keras/overview" rel="nofollow">Keras</a> support.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Supervised Machine Learning</h3><a id="user-content-supervised-machine-learning" class="anchor" aria-label="Permalink: Supervised Machine Learning" href="#supervised-machine-learning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://en.wikipedia.org/wiki/Supervised_learning" rel="nofollow">Supervised learning</a> is when you have input variables <code>X</code> and an output variable <code>Y</code> and you use an algorithm to learn the mapping function from the input to the output: <code>Y = f(X)</code>. The goal is to approximate the mapping function so well that when you have new input data <code>X</code> that you can predict the output variables <code>Y</code> for that data. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Multilayer Perceptron (MLP) or simple Neural Network (NN)</h4><a id="user-content-multilayer-perceptron-mlp-or-simple-neural-network-nn" class="anchor" aria-label="Permalink: Multilayer Perceptron (MLP) or simple Neural Network (NN)" href="#multilayer-perceptron-mlp-or-simple-neural-network-nn"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A <a href="https://en.wikipedia.org/wiki/Multilayer_perceptron" rel="nofollow">multilayer perceptron</a> (MLP) is a class of feedforward artificial neural network (ANN). Multilayer perceptrons are sometimes referred to as "vanilla" neural networks (composed of multiple layers of perceptrons), especially when they have a single hidden layer. It can distinguish data that is not linearly separable.</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="left" width="150"> </th> <th align="left" width="200">Experiment</th> <th align="left" width="140">Model demo & training</th> <th align="left">Tags</th> <th align="left" width="140">Dataset</th> </tr> </thead> <tbody> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/digits_recognition_mlp.png"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/digits_recognition_mlp.png" alt="Handwritten digits recognition (MLP)" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_mlp/digits_recognition_mlp.ipynb"> <b>Handwritten Digits Recognition (MLP)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/DigitsRecognitionMLP" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_mlp/digits_recognition_mlp.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_mlp/digits_recognition_mlp.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>MLP</code> </td> <td> <a href="https://www.tensorflow.org/datasets/catalog/mnist" rel="nofollow"> MNIST </a> </td> </tr> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/sketch_recognition_mlp.png"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/sketch_recognition_mlp.png" alt="Handwritten sketch recognition (MLP)" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_mlp/sketch_recognition_mlp.ipynb"> <b>Handwritten Sketch Recognition (MLP)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/SketchRecognitionMLP" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_mlp/sketch_recognition_mlp.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_mlp/sketch_recognition_mlp.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>MLP</code> </td> <td> <a href="https://github.com/googlecreativelab/quickdraw-dataset"> QuickDraw </a> </td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Convolutional Neural Networks (CNN)</h4><a id="user-content-convolutional-neural-networks-cnn" class="anchor" aria-label="Permalink: Convolutional Neural Networks (CNN)" href="#convolutional-neural-networks-cnn"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A <a href="https://en.wikipedia.org/wiki/Convolutional_neural_network" rel="nofollow">convolutional neural network</a> (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery (photos, videos). They are used for detecting and classifying objects on photos and videos, style transfer, face recognition, pose estimation etc.</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="left" width="150"> </th> <th align="left" width="200">Experiment</th> <th align="left" width="140">Model demo & training</th> <th align="left">Tags</th> <th align="left" width="140">Dataset</th> </tr> </thead> <tbody> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/digits_recognition_cnn.png"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/digits_recognition_cnn.png" alt="Handwritten digits recognition (CNN)" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_cnn/digits_recognition_cnn.ipynb"> <b>Handwritten Digits Recognition (CNN)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/DigitsRecognitionCNN" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_cnn/digits_recognition_cnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_cnn/digits_recognition_cnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>CNN</code> </td> <td> <a href="https://www.tensorflow.org/datasets/catalog/mnist" rel="nofollow"> MNIST </a> </td> </tr> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/sketch_recognition_cnn.png"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/sketch_recognition_cnn.png" alt="Handwritten sketch recognition (CNN)" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_cnn/sketch_recognition_cnn.ipynb"> <b>Handwritten Sketch Recognition (CNN)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/SketchRecognitionCNN" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_cnn/sketch_recognition_cnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/sketch_recognition_cnn/sketch_recognition_cnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>CNN</code> </td> <td> <a href="https://github.com/googlecreativelab/quickdraw-dataset"> QuickDraw </a> </td> </tr> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/rock_paper_scissors_cnn.jpg"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/rock_paper_scissors_cnn.jpg" alt="Rock Paper Scissors" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_cnn/rock_paper_scissors_cnn.ipynb"> <b>Rock Paper Scissors (CNN)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/RockPaperScissorsCNN" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_cnn/rock_paper_scissors_cnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_cnn/rock_paper_scissors_cnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>CNN</code> </td> <td> <a href="http://www.laurencemoroney.com/rock-paper-scissors-dataset/" rel="nofollow"> RPS </a> </td> </tr> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/rock_paper_scissors_mobilenet_v2.jpg"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/rock_paper_scissors_mobilenet_v2.jpg" alt="Rock Paper Scissors" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_mobilenet_v2/rock_paper_scissors_mobilenet_v2.ipynb"> <b>Rock Paper Scissors (MobilenetV2)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/RockPaperScissorsMobilenetV2" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_mobilenet_v2/rock_paper_scissors_mobilenet_v2.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_mobilenet_v2/rock_paper_scissors_mobilenet_v2.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>MobileNetV2</code>, <code>Transfer learning</code>, <code>CNN</code> </td> <td> <a href="http://www.laurencemoroney.com/rock-paper-scissors-dataset/" rel="nofollow"> RPS </a>, <a href="http://image-net.org/explore" rel="nofollow"> ImageNet </a> </td> </tr> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/objects_detection_ssdlite_mobilenet_v2.jpg"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/objects_detection_ssdlite_mobilenet_v2.jpg" alt="Objects detection" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/objects_detection_ssdlite_mobilenet_v2/objects_detection_ssdlite_mobilenet_v2.ipynb"> <b>Objects Detection (MobileNetV2)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/ObjectsDetectionSSDLiteMobilenetV2" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/objects_detection_ssdlite_mobilenet_v2/objects_detection_ssdlite_mobilenet_v2.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/objects_detection_ssdlite_mobilenet_v2/objects_detection_ssdlite_mobilenet_v2.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>MobileNetV2</code>, <code>SSDLite</code>, <code>CNN</code> </td> <td> <a href="http://cocodataset.org/#home" rel="nofollow"> COCO </a> </td> </tr> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/image_classification_mobilenet_v2.jpg"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/image_classification_mobilenet_v2.jpg" alt="Objects detection" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/image_classification_mobilenet_v2/image_classification_mobilenet_v2.ipynb"> <b>Image Classification (MobileNetV2)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/ImageClassificationMobilenetV2" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/image_classification_mobilenet_v2/image_classification_mobilenet_v2.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/image_classification_mobilenet_v2/image_classification_mobilenet_v2.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>MobileNetV2</code>, <code>CNN</code> </td> <td> <a href="http://image-net.org/explore" rel="nofollow"> ImageNet </a> </td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Recurrent Neural Networks (RNN)</h4><a id="user-content-recurrent-neural-networks-rnn" class="anchor" aria-label="Permalink: Recurrent Neural Networks (RNN)" href="#recurrent-neural-networks-rnn"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A <a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow">recurrent neural network</a> (RNN) is a class of deep neural networks, most commonly applied to sequence-based data like speech, voice, text or music. They are used for machine translation, speech recognition, voice synthesis etc.</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="left" width="150"> </th> <th align="left" width="200">Experiment</th> <th align="left" width="140">Model demo & training</th> <th align="left">Tags</th> <th align="left" width="140">Dataset</th> </tr> </thead> <tbody> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/numbers_summation_rnn.png"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/numbers_summation_rnn.png" alt="Numbers summation (RNN)" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/numbers_summation_rnn/numbers_summation_rnn.ipynb"> <b>Numbers Summation (RNN)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/NumbersSummationRNN" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/numbers_summation_rnn/numbers_summation_rnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/numbers_summation_rnn/numbers_summation_rnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>LSTM</code>, <code>Sequence-to-sequence</code> </td> <td> Auto-generated </td> </tr> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/text_generation_shakespeare_rnn.jpg"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/text_generation_shakespeare_rnn.jpg" alt="Shakespeare Text Generation (RNN)" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_shakespeare_rnn/text_generation_shakespeare_rnn.ipynb"> <b>Shakespeare Text Generation (RNN)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/TextGenerationShakespeareRNN" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_shakespeare_rnn/text_generation_shakespeare_rnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_shakespeare_rnn/text_generation_shakespeare_rnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>LSTM</code>, <code>Character-based RNN</code> </td> <td> <a href="https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt" rel="nofollow"> Shakespeare </a> </td> </tr> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/text_generation_wikipedia_rnn.png"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/text_generation_wikipedia_rnn.png" alt="Wikipedia Text Generation (RNN)" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_wikipedia_rnn/text_generation_wikipedia_rnn.ipynb"> <b>Wikipedia Text Generation (RNN)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/TextGenerationWikipediaRNN" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_wikipedia_rnn/text_generation_wikipedia_rnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/text_generation_wikipedia_rnn/text_generation_wikipedia_rnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>LSTM</code>, <code>Character-based RNN</code> </td> <td> <a href="https://www.tensorflow.org/datasets/catalog/wikipedia" rel="nofollow"> Wikipedia </a> </td> </tr> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/recipe_generation_rnn.jpg"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/recipe_generation_rnn.jpg" alt="Recipe Generation (RNN)" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/recipe_generation_rnn/recipe_generation_rnn.ipynb"> <b>Recipe Generation (RNN)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/RecipeGenerationRNN" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/recipe_generation_rnn/recipe_generation_rnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/recipe_generation_rnn/recipe_generation_rnn.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>LSTM</code>, <code>Character-based RNN</code> </td> <td> <a href="https://eightportions.com/datasets/Recipes/" rel="nofollow"> Recipe box </a> </td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Unsupervised Machine Learning</h3><a id="user-content-unsupervised-machine-learning" class="anchor" aria-label="Permalink: Unsupervised Machine Learning" href="#unsupervised-machine-learning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://en.wikipedia.org/wiki/Unsupervised_learning" rel="nofollow">Unsupervised learning</a> is when you only have input data <code>X</code> and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own to discover and present the interesting structure in the data.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Generative Adversarial Networks (GANs)</h4><a id="user-content-generative-adversarial-networks-gans" class="anchor" aria-label="Permalink: Generative Adversarial Networks (GANs)" href="#generative-adversarial-networks-gans"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A <a href="https://en.wikipedia.org/wiki/Generative_adversarial_network" rel="nofollow">generative adversarial network</a> (GAN) is a class of machine learning frameworks where two neural networks contest with each other in a game. Two models are trained simultaneously by an adversarial process. For example a <em>generator</em> ("the artist") learns to create images that look real, while a <em>discriminator</em> ("the art critic") learns to tell real images apart from fakes.</p> <markdown-accessiblity-table><table> <thead> <tr> <th align="left" width="150"> </th> <th align="left" width="200">Experiment</th> <th align="left" width="140">Model demo & training</th> <th align="left">Tags</th> <th align="left" width="140">Dataset</th> </tr> </thead> <tbody> <tr> <td> <a target="_blank" rel="noopener noreferrer" href="/trekhleb/machine-learning-experiments/blob/master/demos/src/images/clothes_generation_dcgan.jpg"><img src="/trekhleb/machine-learning-experiments/raw/master/demos/src/images/clothes_generation_dcgan.jpg" alt="Clothes Generation (DCGAN)" width="150" style="max-width: 100%;"></a> </td> <td> <a href="/trekhleb/machine-learning-experiments/blob/master/experiments/clothes_generation_dcgan/clothes_generation_dcgan.ipynb"> <b>Clothes Generation (DCGAN)</b> </a> </td> <td> <a href="https://trekhleb.github.io/machine-learning-experiments/#/experiments/ClothesGenerationDCGAN" rel="nofollow"> <img src="https://camo.githubusercontent.com/e64ebded70e39dfa3f2007374a3f680264725eab40ced2c0dbb5cb3cde3e5c99/68747470733a2f2f696d672e736869656c64732e696f2f7374617469632f76313f6c6162656c3d2546302539462538452541382532304c61756e6368266d6573736167653d44656d6f26636f6c6f723d677265656e" alt="Launch demo" data-canonical-src="https://img.shields.io/static/v1?label=%F0%9F%8E%A8%20Launch&message=Demo&color=green" style="max-width: 100%;"> </a> <a href="https://nbviewer.jupyter.org/github/trekhleb/machine-learning-experiments/blob/master/experiments/clothes_generation_dcgan/clothes_generation_dcgan.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/7861126a7eb56440456a50288331e87b9604edbaa125354195637561fd400014/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667" alt="Open in Binder" data-canonical-src="https://mybinder.org/badge_logo.svg" style="max-width: 100%;"> </a> <a href="https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/clothes_generation_dcgan/clothes_generation_dcgan.ipynb" rel="nofollow"> <img src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"> </a> </td> <td> <code>DCGAN</code> </td> <td> <a href="https://www.tensorflow.org/datasets/catalog/fashion_mnist" rel="nofollow"> Fashion MNIST </a> </td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How to use this repository locally</h2><a id="user-content-how-to-use-this-repository-locally" class="anchor" aria-label="Permalink: How to use this repository locally" href="#how-to-use-this-repository-locally"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Setup virtual environment for Experiments</h3><a id="user-content-setup-virtual-environment-for-experiments" class="anchor" aria-label="Permalink: Setup virtual environment for Experiments" href="#setup-virtual-environment-for-experiments"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Create "experiments" environment (from the project root folder). python3 -m venv .virtualenvs/experiments # Activate environment. source .virtualenvs/experiments/bin/activate # or if you use Fish... source .virtualenvs/experiments/bin/activate.fish"><pre><span class="pl-c"><span class="pl-c">#</span> Create "experiments" environment (from the project root folder).</span> python3 -m venv .virtualenvs/experiments <span class="pl-c"><span class="pl-c">#</span> Activate environment.</span> <span class="pl-c1">source</span> .virtualenvs/experiments/bin/activate <span class="pl-c"><span class="pl-c">#</span> or if you use Fish...</span> <span class="pl-c1">source</span> .virtualenvs/experiments/bin/activate.fish</pre></div> <p dir="auto">To quit an environment run <code>deactivate</code>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Install dependencies</h3><a id="user-content-install-dependencies" class="anchor" aria-label="Permalink: Install dependencies" href="#install-dependencies"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Upgrade pip and setuptools to the latest versions. pip install --upgrade pip setuptools # Install packages pip install -r requirements.txt"><pre><span class="pl-c"><span class="pl-c">#</span> Upgrade pip and setuptools to the latest versions.</span> pip install --upgrade pip setuptools <span class="pl-c"><span class="pl-c">#</span> Install packages</span> pip install -r requirements.txt</pre></div> <p dir="auto">To install new packages run <code>pip install package-name</code>. To add new packages to the requirements run <code>pip freeze > requirements.txt</code>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Launch Jupyter locally</h3><a id="user-content-launch-jupyter-locally" class="anchor" aria-label="Permalink: Launch Jupyter locally" href="#launch-jupyter-locally"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">In order to play around with Jupyter notebooks and see how models were trained you need to launch a <a href="https://jupyter.org/" rel="nofollow">Jupyter Notebook</a> server.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Launch Jupyter server. jupyter notebook"><pre><span class="pl-c"><span class="pl-c">#</span> Launch Jupyter server.</span> jupyter notebook</pre></div> <p dir="auto">Jupyter will be available locally at <code>http://localhost:8888/</code>. Notebooks with experiments may be found in <code>experiments</code> folder.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Launch demos locally</h3><a id="user-content-launch-demos-locally" class="anchor" aria-label="Permalink: Launch demos locally" href="#launch-demos-locally"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Demo application is made on React by means of <a href="https://github.com/facebook/create-react-app">create-react-app</a>.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Switch to demos folder from project root. cd demos # Install all dependencies. yarn install # Start demo server on http. yarn start # Or start demo server on https (for camera access in browser to work on localhost). yarn start-https"><pre><span class="pl-c"><span class="pl-c">#</span> Switch to demos folder from project root.</span> <span class="pl-c1">cd</span> demos <span class="pl-c"><span class="pl-c">#</span> Install all dependencies.</span> yarn install <span class="pl-c"><span class="pl-c">#</span> Start demo server on http. </span> yarn start <span class="pl-c"><span class="pl-c">#</span> Or start demo server on https (for camera access in browser to work on localhost).</span> yarn start-https</pre></div> <p dir="auto">Demos will be available locally at <code>http://localhost:3000/</code> or at <code>https://localhost:3000/</code>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Convert models</h3><a id="user-content-convert-models" class="anchor" aria-label="Permalink: Convert models" href="#convert-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The <code>converter</code> environment is used to convert the models that were trained during the experiments from <code>.h5</code> Keras format to Javascript understandable formats (<code>tfjs_layers_model</code> or <code>tfjs_graph_model</code> formats with <code>.json</code> and <code>.bin</code> files) for further usage with <a href="https://www.tensorflow.org/js" rel="nofollow">TensorFlow.js</a> in <a href="http://trekhleb.github.io/machine-learning-experiments/" rel="nofollow">Demo application</a>.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Create "converter" environment (from the project root folder). python3 -m venv .virtualenvs/converter # Activate "converter" environment. source .virtualenvs/converter/bin/activate # or if you use Fish... source .virtualenvs/converter/bin/activate.fish # Install converter requirements. pip install -r requirements.converter.txt"><pre><span class="pl-c"><span class="pl-c">#</span> Create "converter" environment (from the project root folder).</span> python3 -m venv .virtualenvs/converter <span class="pl-c"><span class="pl-c">#</span> Activate "converter" environment.</span> <span class="pl-c1">source</span> .virtualenvs/converter/bin/activate <span class="pl-c"><span class="pl-c">#</span> or if you use Fish...</span> <span class="pl-c1">source</span> .virtualenvs/converter/bin/activate.fish <span class="pl-c"><span class="pl-c">#</span> Install converter requirements.</span> pip install -r requirements.converter.txt</pre></div> <p dir="auto">The conversion of <code>keras</code> models to <code>tfjs_layers_model</code>/<code>tfjs_graph_model</code> formats is done by <a href="https://github.com/tensorflow/tfjs/tree/master/tfjs-converter">tfjs-converter</a>:</p> <p dir="auto">For example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="tensorflowjs_converter --input_format keras \ ./experiments/digits_recognition_mlp/digits_recognition_mlp.h5 \ ./demos/public/models/digits_recognition_mlp"><pre>tensorflowjs_converter --input_format keras \ ./experiments/digits_recognition_mlp/digits_recognition_mlp.h5 \ ./demos/public/models/digits_recognition_mlp</pre></div> <blockquote> <p dir="auto"><g-emoji class="g-emoji" alias="warning">⚠️</g-emoji> Converting the models to JS understandable formats and loading them to the browser directly might not be a good practice since in this case the user might need to load tens or hundreds of megabytes of data to the browser which is not efficient. Normally the model is being served from the back-end (i.e. <a href="https://www.tensorflow.org/tfx" rel="nofollow">TensorFlow Extended</a>) and instead of loading it all to the browser the user will do a lightweight HTTP request to do a prediction. But since the <a href="http://trekhleb.github.io/machine-learning-experiments/" rel="nofollow">Demo App</a> is just an experiment and not a production-ready app and for the sake of simplicity (to avoid having an up and running back-end) we're converting the models to JS understandable formats and loading them directly into the browser.</p> </blockquote> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Requirements</h3><a id="user-content-requirements" class="anchor" aria-label="Permalink: Requirements" href="#requirements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Recommended versions:</p> <ul dir="auto"> <li>Python: <code>> 3.7.3</code>.</li> <li>Node: <code>>= 12.4.0</code>.</li> <li>Yarn: <code>>= 1.13.0</code>.</li> </ul> <p dir="auto">In case if you have Python version <code>3.7.3</code> you might experience <code>RuntimeError: dictionary changed size during iteration</code> error when trying to <code>import tensorflow</code> (see the <a href="https://github.com/tensorflow/tensorflow/issues/33183" data-hovercard-type="issue" data-hovercard-url="/tensorflow/tensorflow/issues/33183/hovercard">issue</a>).</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">You might also be interested in</h2><a id="user-content-you-might-also-be-interested-in" 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1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://trekhleb.dev" rel="nofollow">@trekhleb</a></li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="pGRpSuIYdUQb9QzNVjei1SEIcQPSqiR8w8j+VSQmwXOXd1312dR3aukuBbVpZ6FhU8T/C2ka6FwC6VjgbjJqag==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> 🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 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