websites.yaml

- name: "CNN Explainer"
  desc: "An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs)"
  url: https://poloclub.github.io/cnn-explainer/
  repo: https://github.com/poloclub/cnn-explainer
  license: MIT
  writeup: https://arxiv.org/abs/2004.15004
  authors: [Zijie Jay Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, Duen Horng (Polo) Chau]
  tags: [cnn, convnet]
  uses: [demo, tutorial]
  img: https://i.imgur.com/7FGSR7S.png

- name: "Deep Recurrent Nets character generation demo"
  desc: "This demo shows usage of the recurrentjs library train RNN and LSTM networks in JavaScript."
  url: https://cs.stanford.edu/people/karpathy/recurrentjs/
  repo: https://github.com/karpathy/recurrentjs
  license: MIT
  writeup: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
  authors: [Andrej Karpathy]
  tags: [rnn, lstm, nlp]
  uses: [demo, library]
  img: https://cs.stanford.edu/people/karpathy/recurrentjs/eg.png

- name: "ConvNetJS"
  desc: "Various demos of deep learning in JavaScript, using ConvNetJS library."
  url: https://cs.stanford.edu/people/karpathy/convnetjs/
  repo: https://github.com/karpathy/convnetjs
  license: MIT
  writeup: ""
  authors: [Andrej Karpathy]
  tags: [convnet]
  uses: [demo, library]
  img: https://cs.stanford.edu/people/karpathy/convnetjs/cifar10.jpeg

- name: "Support Vector Machine in JavaScript"
  desc: "A lightweight implementation of the sequential minimal optimization algorithm to train a binary SVM model in JavaScript."
  url: https://cs.stanford.edu/~karpathy/svmjs/demo/
  repo: https://github.com/karpathy/svmjs
  license: MIT
  writeup: ""
  authors: [Andrej Karpathy]
  tags: [svm]
  uses: [demo, library, npm]
  img: imgs/svmjs.png

- name: "tSNEJS demo"
  desc: "tSNE used for clustering the 500 most-followed accounts on Twitter."
  url: https://cs.stanford.edu/people/karpathy/tsnejs/
  repo: https://github.com/karpathy/tsnejs
  license: MIT
  writeup: http://karpathy.github.io/2014/07/02/visualizing-top-tweeps-with-t-sne-in-Javascript/
  authors: [Andrej Karpathy]
  tags: [tsne]
  uses: [demo, library]
  img: https://karpathy.github.io/assets/tsne_preview.jpeg

- name: "Random Forest demo in JavaScript"
  desc: "Random Forest implementation for JavaScript. Supports arbitrary weak learners."
  url: https://cs.stanford.edu/~karpathy/svmjs/demo/demoforest.html
  repo: https://github.com/karpathy/forestjs
  license: MIT
  writeup: ""
  authors: [Andrej Karpathy]
  tags: [tsne]
  uses: [demo, library]
  img: imgs/forestjs.png

- name: "Keras.js"
  desc: "Keras.js is a library enabling running Keras models in the browser, with GPU support provided by WebGL 2."
  url: https://transcranial.github.io/keras-js/#/
  repo: https://github.com/transcranial/keras-js
  license: MIT
  writeup: ""
  authors: [Leon Chen]
  tags: [convnet, rnn]
  uses: [demo, library]
  img: https://transcranial.github.io/keras-js/demos/assets/resnet50.png

- name: "Interactive visualization of word analogies in GloVe"
  desc: "king - man + woman is queen; but why? Explore word vectors interactively."
  url: https://lamyiowce.github.io/word2viz/
  repo: https://github.com/lamyiowce/word2viz
  license: MIT
  writeup: http://p.migdal.pl/2017/01/06/king-man-woman-queen-why.html
  authors: [Julia Bazińska, Piotr Migdał]
  tags: [nlp, word2vec]
  uses: [demo]
  img: https://lamyiowce.github.io/word2viz/word2viz_screenshot.png

- name: "A Neural Network Playground"
  desc: "Interactive visualization of simple neural networks, written in typescript using d3.js."
  url: http://playground.tensorflow.org/
  repo: https://github.com/tensorflow/playground
  license: Apache
  writeup: ""
  authors: [Daniel Smilkov, Shan Carter]
  tags: [mlp]
  uses: [demo]
  img: http://playground.tensorflow.org/preview.png

- name: "A visual introduction to machine learning (Part 1)"
  url: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
  desc: "A demo of using a decision tree algorithm to distinguish houses in New York from houses in San Francisco"
  repo: ""
  license: ""
  writeup: ""
  authors: [Stephanie Yee, Tony Chu]
  tags: [decision-tree]
  uses: [demo, tutorial]
  img: http://www.r2d3.us/static/pages/decision-trees-part-1/preview-en.png

- name: "A visual introduction to machine learning (Part 2)"
  desc: Model Tuning and the Bias-Variance Tradeoff
  url: http://www.r2d3.us/visual-intro-to-machine-learning-part-2/
  repo: ""
  license: ""
  writeup: ""
  authors: [Stephanie Yee, Tony Chu]
  tags: [decision-tree, overfitting]
  uses: [demo, tutorial]
  img: http://www.r2d3.us/static/pages/decision-trees-part2-v2/part2-preview-en.png

- name: "D3 Graph Theory"
  desc: "Learn graph theory using interactive visualizations made in D3.js."
  url: https://mrpandey.github.io/d3graphTheory/index.html
  repo: https://github.com/mrpandey/d3graphTheory
  license: GPL
  writeup: ""
  authors: [Avinash Pandey]
  tags: [graph]
  uses: [demo, tutorial]
  img: https://i.imgur.com/MAvlQgj.png

- name: "Immersive linear algebra"
  desc: "The world's first linear algebra book with fully interactive figures."
  url: http://immersivemath.com/ila/index.html
  repo: ""
  license: ""
  writeup: ""
  authors: [J. Ström, K. Åström, and T. Akenine-Mölle]
  tags: [math, linear-algebra]
  uses: [book]
  img: imgs/immersivemath.png

- name: "Seeing Theory"
  desc: "A visual introduction to probability and statistics"
  url: http://students.brown.edu/seeing-theory/
  repo: https://github.com/seeingtheory/Seeing-Theory
  license: Apache
  writeup: ""
  authors: [Daniel Kunin, Jingru Guo, Tyler Dae Devlin, Daniel Xiang]
  tags: [statistics]
  uses: [tutorial]
  img: https://students.brown.edu/seeing-theory/img/share/home.png

- name: "tSNE for the Web"
  desc: "TensorFlow.js Powered tSNE Implementation"
  url: https://nicola17.github.io/tfjs-tsne-demo/
  repo: https://github.com/tensorflow/tfjs-tsne
  license: Apache
  writeup: https://ai.googleblog.com/2018/06/realtime-tsne-visualizations-with.html
  authors: [Nicola Pezzotti, Alexander Mordvintsev, Thomas Hollt, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova]
  tags: [tsne]
  uses: [demo, tensorflow.js, npm]
  img: https://3.bp.blogspot.com/-NE01azL_JxU/Wxli17oYNzI/AAAAAAAACxQ/axOI2yy-Ft0QbqaekOyemm5Xn0wAFvRUwCLcBGAs/s1600/image2.gif

- name: "GRAIL Text Recognizer"
  desc: "An active essay revisiting Gabriel Groner's GRAIL handwriting recognizer from the 1960s"
  url: https://jackschaedler.github.io/handwriting-recognition/
  repo: https://github.com/jackschaedler/handwriting-recognition
  license: MIT
  writeup: ""
  authors: [Jack Schaedler]
  tags: [handwriting]
  uses: [demo, tutorial]
  img: imgs/handwriting-recognition.png

- name: "pix2pix: Image-to-Image Demo"
  desc: "Tensorflow port of Image-to-Image Translation with Conditional Adversarial Nets"
  url: https://affinelayer.com/pixsrv/
  repo: https://github.com/affinelayer/pix2pix-tensorflow
  license: MIT
  writeup: ""
  authors: [Christopher Hesse]
  tags: [gan, deep-learning]
  uses: [demo, backend-dependent]
  img: https://raw.githubusercontent.com/affinelayer/pix2pix-tensorflow/master/docs/examples.jpg

- name: "nlp-compromise"
  desc: "Modest natural-language processing in JavaScript"
  url: http://compromise.cool/
  repo: https://github.com/spencermountain/compromise
  license: MIT
  writeup: ""
  authors: [Spencer Kelly]
  tags: [nlp]
  uses: [demo, library]
  img: imgs/compromise.png

- name: "NeuroJS"
  desc: "A JavaScript deep learning and reinforcement learning library"
  url: https://janhuenermann.com/projects/learning-to-drive
  repo: https://github.com/janhuenermann/neurojs
  license: MIT
  writeup: ""
  authors: [Jan Hünermann]
  tags: [deep-learning, reinforcement-learning]
  uses: [demo, library]
  img: https://raw.githubusercontent.com/janhuenermann/neurojs/experimental/images/logo-with-demo.png

- name: "DeepForge"
  desc: "A modern development environment for deep learning"
  url: http://deepforge.org/
  repo: https://github.com/deepforge-dev/deepforge
  license: Apache
  writeup: ""
  authors: [Brian Broll]
  tags: [deep-learning]
  uses: [demo, library]
  img: https://raw.githubusercontent.com/deepforge-dev/deepforge/master/images/overview.png

- name: "synaptic"
  desc: "The javascript architecture-free neural network library for node.js and the browser"
  url: http://caza.la/synaptic/
  repo: https://github.com/cazala/synaptic
  license: MIT
  writeup: ""
  authors: [Brian Broll]
  tags: [deep-learning]
  uses: [demo, library]
  img: imgs/synaptic.png

- name: "Machine learning library for Node.js"
  desc: "Logistic regression, SVM linear and radial, kNN"
  url: http://joonku.com/project/machine_learning
  repo: https://github.com/junku901/machine_learning
  license: MIT
  writeup: ""
  authors: [Joon-Ku Kang]
  tags: [machine-learning]
  uses: [demo, library, npm]

- name: "Neural Slime Volleyball"
  desc: "HTML5-JS Slime Volleyball clone created using ConvNetJS."
  url: http://otoro.net/slimevolley/
  repo: https://github.com/hardmaru/neuralslimevolley
  license: GPL
  writeup: http://blog.otoro.net/2015/03/28/neural-slime-volleyball/
  authors: [hardmaru]
  tags: [deep-learning]
  uses: [demo]
  img: imgs/slimevolley.png

- name: "Quick, draw!"
  desc: "Can a neural network learn to recognize your doodling? The game uses the world’s largest doodling dataset."
  url: https://quickdraw.withgoogle.com/
  repo: ""
  license: ""
  writeup: https://ai.googleblog.com/2017/04/teaching-machines-to-draw.html
  authors: [hardmaru]
  tags: [deep-learning]
  uses: [game, backend-dependent]
  img: https://1.bp.blogspot.com/-X0o-v6SQXtI/WO6R8X22wlI/AAAAAAAABts/Qbvu5QoVwZIfKB-zEV7Po_Juh9AqEb28gCLcB/s1600/twitter_cover.png

- name: "SegNet"
  desc: "A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling"
  url: https://mi.eng.cam.ac.uk/projects/segnet/#demo
  repo: https://github.com/alexgkendall/caffe-segnet
  license: CC BY NC
  writeup: https://mi.eng.cam.ac.uk/projects/segnet/#research
  authors: [Alex Kendall, Vijay Badrinarayanan, Roberto Cipolla]
  tags: [deep-learning, cnn, segmentation]
  uses: [demo, backend-dependent]
  img: imgs/segnet.png

- name: "Caffe Demos"
  desc: "Web image classification demo"
  url: http://demo.caffe.berkeleyvision.org/
  repo: ""
  license: BSD 2-Clause license
  writeup: http://caffe.berkeleyvision.org/
  authors: [Yangqing Jia]
  tags: [deep-learning, cnn, classification, image-classification]
  uses: [demo, backend-dependent]
  img: imgs/caffe-demos.png

- name: "LSTM Music Maker"
  desc: "How evlolved LSTMs improvise on a melody you specify?"
  url: https://www.sentient.ai/sentient-labs/ea/lstm-music/
  repo: ""
  license: ""
  writeup: https://arxiv.org/abs/1803.04439
  authors: [Aditya Rawal, Risto Miikkulainen]
  tags: [deep-learning, lstm, music]
  uses: [demo]
  img: imgs/lstm-music-maker.png

- name: "OMNI Draw"
  desc: "How do the learned models perceive the characters you draw?"
  url: https://www.sentient.ai/sentient-labs/ea/omni-draw/
  repo: ""
  license: ""
  writeup: https://arxiv.org/abs/1803.03745
  authors: [Jason Liang, Elliot Meyerson, Risto Miikkulainen]
  tags: [deep-learning, cmtr, omniglot]
  uses: [demo]
  img: imgs/omni-draw.png

- name: "Celeb Match"
  desc: "Which celebrity faces match best the attribute values you specify?"
  url: https://www.sentient.ai/sentient-labs/ea/celeb-match/
  repo: ""
  license: ""
  writeup: https://arxiv.org/abs/1803.04062
  authors: [Elliot Meyerson, Risto Miikkulainen]
  tags: [deep-learning, pta]
  uses: [demo]
  img: imgs/celeb-match.png

- name: "Neural Titanic"
  desc: "This visualization uses TensorFlow.js to train a neural network on the titanic dataset"
  url: https://andrewnetwork.github.io/NeuralTitanic/dist/
  repo: https://github.com/Andrewnetwork/NeuralTitanic
  license: MIT
  writeup: http://kexp.io/intro_tensorflowjs/
  authors: [Andrew Ribeiro]
  tags: [mlp, titanic]
  uses: [demo, tensorflowjs]
  img: imgs/neural-titanic.png

- name: "Backpropagation algorithm"
  desc: "The backpropagation algorithm is essential for training large neural networks quickly. This article explains how the algorithm works."
  url: https://google-developers.appspot.com/machine-learning/crash-course/backprop-scroll/
  repo: ""
  license: ""
  writeup: ""
  authors: [Daniel Smilkov]
  tags: [neural-networks, math]
  uses: [tutorial]
  img: imgs/backpropagation-algorithm.png

- name: "Visual Search"
  desc: "This website uses deep neural networks to compute a fingerprint of a query image and retrieves images with similar visual features."
  url: https://thomasdelteil.github.io/VisualSearch_MXNet/
  repo: "https://github.com/ThomasDelteil/VisualSearch_MXNet"
  license: ""
  writeup: ""
  authors: [Thomas Delteil]
  tags: [neural-networks, deep-learning, search, mxnet]
  uses: [tutorial, demo, backend-dependent]
  img: imgs/visual-search.png

- name: bayes.js
  desc: "An implementation of a small MCMC framework and some likelihood functions for doing Bayes stats in the browser."
  url: https://github.com/rasmusab/bayes.js
  repo: https://github.com/rasmusab/bayes.js
  license: MIT
  writeup: ""
  authors: [Rasmus Bååth]
  tags: [bayesian, mcmc, statistics]
  uses: [demo, library]
  img: https://raw.githubusercontent.com/rasmusab/bayes.js/master/media/normal_model_plotly.png

- name: Basic Neural Network Math
  desc: "A Visual And Interactive Look at Basic Neural Network Math"
  url: https://jalammar.github.io/feedforward-neural-networks-visual-interactive/
  repo: https://github.com/jalammar/jalammar.github.io/blob/master/_posts/2018-03-23-feedforward-neural-networks-visual-interactive.md
  license: ""
  writeup: ""
  authors: [Jay Alammar]
  tags: [neural-networks]
  uses: [tutorial]
  img: https://jalammar.github.io/images/two-input-one-output-sigmoid-network.png

- name: Seedbank
  desc: "Collection of Interactive Machine Learning Examples"
  url: http://tools.google.com/seedbank/
  repo: ""
  license: ""
  writeup: https://medium.com/tensorflow/seedbank-discover-machine-learning-examples-2ff894542b57
  authors: [Mike Tyka, Sures Kumar Thoddu Srinivasan, Chris Boudreaux, Simon Doury, Harini Krishnamurthy, Mike Dory, Gabriel Schubiner, Kyle Pedersen, Artists & Machine Intelligence, Colaboratory teams]
  tags: [neural-networks, deep-learning]
  uses: [collection, backend-dependent]
  img: https://tools.google.com/seedbank/static/images/quick-start.gif

- name: "The Beginner's Guide to Dimensionality Reduction"
  desc: "Explore the methods that data scientists use to visualize high-dimensional data."
  url: https://idyll.pub/post/dimensionality-reduction-293e465c2a3443e8941b016d/
  repo: https://github.com/mathisonian/dimensionality-reduction
  license: MIT
  writeup: ""
  authors: [Matthew Conlen, Fred Hohman]
  tags: [pca, tsne]
  uses: [tutorial]
  img: https://idyll.pub/post/dimensionality-reduction-293e465c2a3443e8941b016d/static/images/share.png

- name: "Visualizing K-Means Clustering"
  desc: "The k-means algorithm captures the insight that each point in a cluster should be near to the center of that cluster."
  url: https://www.naftaliharris.com/blog/visualizing-k-means-clustering/
  repo: ""
  license: ""
  writeup: ""
  authors: [Naftali Harris]
  tags: [kmeans]
  uses: [tutorial]
  img: imgs/visualizing-k-means-clustering.png
  date: "2014-01-19"

- name: "Matrix factorization visualized"
  desc: "Matrix factorization implemented in pure JavaScript. Also for sparse data and logistic regression."
  url: https://p.migdal.pl/matrix-decomposition-viz/
  repo: https://github.com/stared/matrix-decomposition-viz
  license: MIT
  writeup: ""
  authors: [Piotr Migdał]
  tags: [matrix-factorization]
  uses: [demo]
  img: imgs/matrix-decomposition-viz.png
  date: "2017-10-17"

- name: "AI Experiments - Experiments with Google"
  desc: "AI Experiments is a showcase for simple experiments that make it easier for anyone to start exploring machine learning, through pictures, drawings, language, music, and more."
  url: https://experiments.withgoogle.com/collection/ai
  repo: ""
  license: ""
  writeup: ""
  authors: [Google]
  tags: [matrix-factorization]
  uses: [collection]
  img: imgs/experiments-with-google.png

- name: "A New Angle on L2 Regularization"
  desc: "An explorable explanation on the phenomenon of adversarial examples in linear classification and its relation to L2 regularization."
  url: https://thomas-tanay.github.io/post--L2-regularization/
  repo: https://github.com/thomas-tanay/post--L2-regularization
  license: ""
  writeup: ""
  authors: [Thomas Tanay, Lewis D Griffin]
  tags: [svm, overfitting, adversarial-examples]
  uses: [tutorial, paper]
  img: "imgs/a-new-angle-on-l2-reg.png"
  date: "2018-06-21"

- name: "Support Vector Machine Explorer"
  desc: "This is a demo of the Dash interactive Python framework developed by Plotly."
  url: https://dash-svm.plot.ly/
  repo: https://github.com/plotly/dash-svm
  license: MIT
  writeup: ""
  authors: [Xing Han Lu]
  tags: [svm]
  uses: [demo, backend-dependent, plotly, scikit-learn]
  img: imgs/dash-svm-plot-ly.png
  date: "2018-08-09"

- name: "GAN lab"
  desc: "Play with Generative Adversarial Networks (GANs) in your browser!"
  url: https://poloclub.github.io/ganlab/
  repo: https://github.com/poloclub/ganlab
  license: Apache
  writeup: ""
  authors: [Minsuk Kahng, Nikhil Thorat, Polo Chau, Fernanda Viégas, and Martin Wattenberg]
  tags: [GAN, deep-learning, tensorflowjs]
  uses: [demo]
  img: imgs/ganlab.png
  date: "2018-09-04"
- name: "Semantic Calculator"
  desc: "Do math with word embeddings!"
  url: https://semantic.a9.io/
  repo: ""
  license: ""
  writeup: ""
  authors: [Max Krieger]
  tags: [word2vec, glove, word-embeddings]
  uses: [demo, backend-dependent]
  img: imgs/semantic-calculator.png
  date: "2018-09-29"

- name: "ml5.js"
  desc: "ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies."
  url: https://ml5js.org/
  repo: https://github.com/ml5js/ml5-library
  license: MIT
  writeup: https://towardsdatascience.com/introduction-to-ml5-js-3fe51d6a4661
  authors: [NYU ITP teachers, residents and students]
  tags: [machine-learning, tensorflowjs]
  uses: [tutorial, demo, library]
  img: imgs/ml5.png

- name: "Drum Patterns from Latent Space"
  desc: "A latent explorable space with some recognizable genre areas."
  url: http://altsoph.com/pp/dsp/map.html#
  repo: https://github.com/altsoph/drum_space
  license: MIT
  writeup: https://towardsdatascience.com/drum-patterns-from-latent-space-23d59dd9d827
  authors: [Aleksey Tikhonov]
  tags: [music, latent]
  uses: [demo]
  img: imgs/drum_space.png
  date: "2019-03-10"