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+# tSNEJS
+
+tSNEJS is an implementation of t-SNE visualization algorithm in Javascript. 
+
+t-SNE is a visualization algorithm that embeds things in 2 or 3 dimensions. If you have some data and you can measure their pairwise differences, t-SNE visualization can help you identify clusters in your data. See example below.
+
+## Online demo
+The main project website has a [live example](https://cs.stanford.edu/people/karpathy/tsnejs/) and more description.
+
+## Research Paper
+The algorithm was originally described in this paper:
+
+ L.J.P. van der Maaten and G.E. Hinton.
+ Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research
+ 9(Nov):2579-2605, 2008.
+
+You can find the PDF [here](https://jmlr.csail.mit.edu/papers/volume9/vandermaaten08a/vandermaaten08a.pdf).
+
+## Example code
+Import tsne.js into your document: `<script src="tsne.js"></script>`
+And then here is some example code:
+
+ var opt = {epsilon: 10}; // epsilon is learning rate (10 = default)
+ var tsne = new tsnejs.tSNE(opt); // create a tSNE instance
+
+ // initialize data. Here we have 3 points and some example pairwise dissimilarities
+ var dists = [[1.0, 0.1, 0.2], [0.1, 1.0, 0.3], [0.2, 0.1, 1.0]];
+ tsne.initDataDist(dists);
+
+ for(var k = 0; k &lt 500; k++) {
+  tsne.step(); // every time you call this, solution gets better
+ }
+
+ var Y = tsne.getSolution(); // Y is an array of 2-D points that you can plot
+
+The data can be passed to tSNEJS as a set of high-dimensional points using the `tsne.initDataRaw(X)` function, where X is an array of arrays (high-dimensional points that need to be embedded). The algorithm computes the Gaussian kernel over these points and then finds the appropriate embedding.
+
+## About
+Send questions to [@karpathy](https://twitter.com/karpathy).