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added itp camp 2017 class
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@genekogan
genekogan committed Jun 23, 2017
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32 changes: 32 additions & 0 deletions _data/guides.json
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"fundamentals": {
"title": "Fundamentals of machine learning",
"description": "Mathematical precepts for machine learning",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/fundamentals.ipynb",
"thumb":"/images/guides_thumbs/fundamentals.jpg",
"summary":[
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"simple": {
"title": "Ordinary neural networks",
"description": "How to train a simple feedforward neural network",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/simple_neural_networks.ipynb",
"thumb":"/images/guides_thumbs/neural-networks.jpg",
"summary":[
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"cnn": {
"title": "Convolutional neural networks",
"description": "How to train a convolutional neural network",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/convolutional_neural_networks.ipynb",
"thumb":"/images/guides_thumbs/convnets.jpg",
"summary":[
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"transfer-learning": {
"title": "Transfer learning",
"description": "Training accurate image classifiers on small datasets",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/transfer-learning.ipynb",
"thumb":"/images/guides_thumbs/transfer-learning.jpg",
"summary":[
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"rnn": {
"title": "Recurrent neural networks",
"description": "Introduction to recurrent neural nets and LSTMs",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/recurrent_neural_networks.ipynb",
"thumb":"/images/guides_thumbs/rnn.jpg",
"summary":[
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"seq2seq": {
"title": "Sequence to sequence models",
"description": "Using recurrent neural nets for sequence to sequence models",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/sequence_to_sequence.ipynb",
"thumb":"/images/guides_thumbs/seq2seq.jpg",
"summary":[
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"eigenfaces": {
"title": "Eigenfaces",
"description": "Understand dimensionality reduction and generate weird faces",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/Eigenfaces.ipynb",
"thumb":"/images/guides_thumbs/eigenfaces.jpg",
"summary":[
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"image-search": {
"title": "Reverse image search",
"description": "How to do reverse image search, i.e. find similar images to query image",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/image-search.ipynb",
"thumb":"/images/guides_thumbs/image-search.jpg",
"summary":[
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"image-path": {
"title": "Find shortest path between two images",
"description": "How to find the shortest path between two images",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/image-path.ipynb",
"thumb":"/images/guides_thumbs/image-path.jpg",
"summary":[
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"image-tsne": {
"title": "Clustering images with t-SNE",
"description": "How to cluster a collection of images in 2d with a t-SNE",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/image-tsne.ipynb",
"thumb":"/images/guides_thumbs/image-tsne.jpg",
"summary":[
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"audio-tsne": {
"title": "Clustering sounds with t-SNE",
"description": "How to cluster a collection of sounds or audio clips in 2d with a t-SNE",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/audio-tsne.ipynb",
"thumb":"/images/guides_thumbs/audio-tsne.jpg",
"summary":[
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"text-retrieval": {
"title": "Reverse text search",
"description": "Representing text for search by document",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/text-retrieval.ipynb",
"thumb":"/images/guides_thumbs/text-retrieval.jpg",
"summary":[
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"neural-painter": {
"title": "Neural net painter",
"description": "Fun experiment teaching a neural net to \"paint\" an image",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/neural-net-painter.ipynb",
"thumb":"/images/guides_thumbs/neural-net-painter.jpg",
"summary":[
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"word2vec": {
"title": "Word vectors",
"description": "Deriving word vectors/embeddings from a text corpus",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/word2vec_tsne.ipynb",
"thumb":"/images/guides_thumbs/word2vec.jpg",
"summary":[
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"qlearn": {
"title": "Q-Learning",
"description": "Introduction to Q Learning",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/q_learning.ipynb",
"thumb":"/images/guides_thumbs/q-learning.jpg",
"summary":[
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"qnets": {
"title": "Deep Q-Networks",
"description": "Using deep Q networks for advanced reinforcement learning",
"category": "python",
"link":"https://github.com/ml4a/ml4a-guides/tree/master/notebooks/deep_q_networks.ipynb",
"thumb":"/images/guides_thumbs/atari-dqn.jpg",
"summary":[
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"AudioClassifier": {
"title": "Audio classifier",
"description": "Audio classifier",
"category": "openframeworks",
"link":"/guides/AudioClassifier",
"thumb":"/images/guides_thumbs/audio-classifier.jpg",
"summary":[
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"AudioTSNEViewer": {
"title": "Audio t-SNE viewer",
"description": "Audio t-SNE viewer",
"category": "openframeworks",
"link":"/guides/AudioTSNEViewer",
"thumb":"/images/guides_thumbs/audio-tsne-viewer.jpg",
"summary":[
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"ConvnetOSC": {
"title": "ConvnetOSC",
"description": "ConvnetOSC",
"category": "openframeworks",
"link":"/guides/ConvnetOSC",
"thumb":"/images/guides_thumbs/convnetOSC.jpg",
"summary":[
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"ConvnetClassifier": {
"title": "Convnet classifier",
"description": "Convnet classifier",
"category": "openframeworks",
"link":"/guides/ConvnetClassifier",
"thumb":"/images/guides_thumbs/convnet-classifier.jpg",
"summary":[
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"ConvnetRegressor": {
"title": "Convnet regressor",
"description": "Convnet regressor",
"category": "openframeworks",
"link":"/guides/ConvnetRegressor",
"thumb":"/images/guides_thumbs/convnet-regressor.jpg",
"summary":[
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"DoodleClassifier": {
"title": "Doodle classifier",
"description": "Doodle classifier",
"category": "openframeworks",
"link":"/guides/DoodleClassifier",
"thumb":"/images/guides_thumbs/doodle-classifier.jpg",
"summary":[
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"FaceClassifier": {
"title": "Face classifier",
"description": "Face classifier",
"category": "openframeworks",
"link":"/guides/FaceClassifier",
"thumb":"/images/guides_thumbs/face-classifier.jpg",
"summary":[
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"FaceRegressor": {
"title": "Face regressor",
"description": "Face regressor",
"category": "openframeworks",
"link":"/guides/FaceRegressor",
"thumb":"/images/guides_thumbs/face-regressor.jpg",
"summary":[
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"Gobot": {
"title": "Gobot",
"description": "Gobot",
"category": "openframeworks",
"link":"/guides/Gobot",
"thumb":"/images/guides_thumbs/goboard.jpg",
"summary":[
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"ImageTSNEViewer": {
"title": "Image t-SNE viewer",
"description": "Image t-SNE viewer",
"category": "openframeworks",
"link":"/guides/ImageTSNEViewer",
"thumb":"/images/guides_thumbs/image-tsne.jpg",
"summary":[
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"ImageTSNELive": {
"title": "Image t-SNE live",
"description": "Image t-SNE live",
"category": "openframeworks",
"link":"/guides/ImageTSNELive",
"thumb":"/images/guides_thumbs/image-tsne-live.jpg",
"summary":[
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"Pix2Pix": {
"title": "pix2pix tutorial",
"description": "pix2pix tutorial",
"category": "openframeworks",
"link":"/guides/Pix2Pix",
"thumb":"/images/guides_thumbs/pix2pix.jpg",
"summary":[
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"ReverseImageSearchFast": {
"title": "Reverse image search (fast)",
"description": "Reverse image search",
"category": "openframeworks",
"link":"/guides/ReverseImageSearchFast",
"thumb":"/images/guides_thumbs/reverse-image-search-fast.jpg",
"summary":[
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"ReverseObjectSearchFast": {
"title": "Reverse object search",
"description": "Reverse object search",
"category": "openframeworks",
"link":"/guides/ReverseObjectSearchFast",
"thumb":"/images/guides_thumbs/reverse-object-search.jpg",
"summary":[
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"YoloLive": {
"title": "Yolo (fast object detection) live",
"description": "Yolo (fast object detection) live",
"category": "openframeworks",
"link":"/guides/YoloLive",
"thumb":"/images/guides_thumbs/yolo-live.jpg",
"summary":[
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"Contribute": {
"title": "Contribute!",
"description": "Contribute!",
"category": "python openframeworks",
"link":"/guides/Contribute/",
"thumb":"/images/guides_thumbs/default.jpg",
"summary":[
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33 changes: 31 additions & 2 deletions _data/lectures.json
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@@ -1,10 +1,39 @@
{"opendot":[{
{"misc":[{
"youtube_id": "mbkbMvMxLmg",
"title": "From principal components to puppyslugs",
"date": "6/21/2017",
"thumbnail": "/images/classes/misc/thumbnail_01.png",
"dropbox": "",
"bookmarks": [
{"title":"Generative models", "m":4, "s":10, "disp":"4:10"},
{"title":"Why sampling images is hard", "m":5, "s":58, "disp":"5:58"},
{"title":"Principal component analysis", "m":9, "s":57, "disp":"9:57"},
{"title":"Eigenfaces", "m":15, "s":15, "disp":"15:15"},
{"title":"Neural networks", "m":29, "s":28, "disp":"29:28"},
{"title":"Representation learning and feature extraction", "m":48, "s":26, "disp":"48:26"},
{"title":"Autoencoders", "m":61, "s":31, "disp":"1:01:31"},
{"title":"Generative adversarial networks", "m":67, "s":31, "disp":"1:07:31"},
{"title":"BEGAN, InfoGAN, DiscoGAN, StackGAN, ArtGAN", "m":77, "s":44, "disp":"1:17:44"},
{"title":"Deep generator networks", "m":81, "s":50, "disp":"1:21:50"},
{"title":"Conditional GANs (pix2pix)", "m":86, "s":25, "disp":"1:26:25"},
{"title":"CycleGANs, horse2zebra", "m":94, "s":27, "disp":"1:34:27"},
{"title":"Skip-thought vectors and WaveNets", "m":97, "s":11, "disp":"1:37:11"},
{"title":"Class synthesis, deepdream, and puppyslugs", "m":101, "s":08, "disp":"1:41:08"}
],
"summary": [
"Generative modeling of images",
"Principal component analysis and Eigenfaces",
"Autoencoders, generative adversarial networks, pix2pix",
"Deepdream, class synthesis, and puppyslugs",
"Taught by <a href=\"https://twitter.com/genekogan\">@genekogan</a>"
]}
],
"opendot":[{
"youtube_id": "yHOmMCY589Y",
"title": "Why machine learning for artists",
"date": "11/21/2016",
"thumbnail": "/images/classes/opendot/thumbnail_01.png",
"dropbox": "https://www.dropbox.com/s/fxz3pbyoj2se91j/opendot_11.21_01.mp4?dl=1",

"bookmarks": [
{"title":"Introduction, about the class", "m":0, "s":0, "disp":"0:00"},
{"title":"Neither democrats nor dictators; Why ML for artists", "m":8, "s":4, "disp":"8:04"},
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2 changes: 1 addition & 1 deletion _includes/class_preview.html
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<div class="class_meta">
<a href="/classes/{{class}}/0{{c_idx}}" class="class_title">{{data.title}}</a>
<br/>
<a href="/classes/{{class}}/0{{c_idx}}" class="class_date">{{data.date}}</a> [<a href="{{data.dropbox}}">download</a>]
<a href="/classes/{{class}}/0{{c_idx}}" class="class_date">{{data.date}}</a> {% if data.dropbox != "" %}[<a href="{{data.dropbox}}">download</a>]{% endif %}
<span class="class_contents">
<ul>
{% for s in data.summary %}
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6 changes: 4 additions & 2 deletions _includes/guide_preview2.md
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{% assign guide = site.data.guides[name] %}
{% assign title = guide.title %}
{% assign description = guide.description %}
{% assign category = guide.category %}
{% assign summary = guide.summary %}
{% assign link = guide.link %}
{% assign thumb = guide.thumb %}


<div class="project">
<div class="project {{category}}">
<a href="{{link}}">
<img src="{{thumb}}">
<div class="overlay">
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</div>
</div>
</a>
</div>
</div>

4 changes: 4 additions & 0 deletions _pages/classes.md
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title: ml4a classes
---


{% include class_preview.html class="misc" index=0 %}


<div class="class_main">
<div class="class_thumb">
<a href="/classes/opendot/"><img class="thumby" src="/images/classes/opendot/thumbnail_06.png"></a>
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31 changes: 29 additions & 2 deletions _pages/guides2.md
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<div id="platforms">
<div class="platform">Keras</div>
<div class="platform">openFrameworks</div>
<div id="platform_all" class="platform"><a href="javascript:displayAll();">All</a></div>
<div id="platform_python" class="platform"><a href="javascript:displayByKey('python');">Keras / Tensorflow</a></div>
<div id="platform_openframeworks" class="platform"><a href="javascript:displayByKey('openframeworks');">openFrameworks</a></div>
</div>


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{% include guide_preview2.md name="Contribute" %}






<script>
function displayAll() {
var d = document.getElementsByClassName("project");
for(var i = 0; i < d.length; i++){ d[i].style.display = "inline-block"; }
};
function hideAll() {
var d = document.getElementsByClassName("project");
for(var i = 0; i < d.length; i++){ d[i].style.display = "none"; }
};
function displayByKey(keyword) {
hideAll();
d = document.getElementsByClassName("project "+keyword);
for(var i = 0; i < d.length; i++){ d[i].style.display = "inline-block"; }
// document.getElementsByClassName("platform").forEach(function (d){
// d.style.border = "1px solid #000";
// });
document.getElementById("platform_"+keyword).style.border = "3px solid #0a0";
// document.getElementsByClassName("displayAll")[0].style.display = "block";
};

</script>
2 changes: 1 addition & 1 deletion classes/itp-S16/07/index.html
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<h2>Class notes</h2>


<h4>This 3-hour session was given during <a href="itp.nyu.edu/camp2016/session/88">ITP camp</a>.</h4>
<h4>This 3-hour session was given during <a href="https://itp.nyu.edu/camp2016/session/88">ITP camp</a> 2016.</h4>
<p/>
<h4>It is a condensed version of <a href="/classes/itp-S16">Machine Learning for Artists</a> taught in Spring 2016</h4>
<p/>&nbsp;
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