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+
+# ConvNetJS
+
+ConvNetJS is a Javascript implementation of Neural networks, together with nice browser-based demos. It currently supports:
+
+- Common **Neural Network modules** (fully connected layers, non-linearities)
+- Classification (SVM/Softmax) and Regression (L2) **cost functions**
+- Ability to specify and train **Convolutional Networks** that process images
+- An experimental **Reinforcement Learning** module, based on Deep Q Learning
+
+For much more information, see the main page at [convnetjs.com](http://convnetjs.com)
+
+**Note**: I am not actively maintaining ConvNetJS anymore because I simply don't have time. I think the npm repo might not work at this point.
+
+## Online Demos
+- [Convolutional Neural Network on MNIST digits](http://cs.stanford.edu/~karpathy/convnetjs/demo/mnist.html)
+- [Convolutional Neural Network on CIFAR-10](http://cs.stanford.edu/~karpathy/convnetjs/demo/cifar10.html)
+- [Toy 2D data](http://cs.stanford.edu/~karpathy/convnetjs/demo/classify2d.html)
+- [Toy 1D regression](http://cs.stanford.edu/~karpathy/convnetjs/demo/regression.html)
+- [Training an Autoencoder on MNIST digits](http://cs.stanford.edu/~karpathy/convnetjs/demo/autoencoder.html)
+- [Deep Q Learning Reinforcement Learning demo](http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html)
+- [Image Regression ("Painting")](http://cs.stanford.edu/~karpathy/convnetjs/demo/image_regression.html)
+- [Comparison of SGD/Adagrad/Adadelta on MNIST](http://cs.stanford.edu/people/karpathy/convnetjs/demo/trainers.html)
+
+## Example Code
+
+Here's a minimum example of defining a **2-layer neural network** and training
+it on a single data point:
+
+```javascript
+// species a 2-layer neural network with one hidden layer of 20 neurons
+var layer_defs = [];
+// input layer declares size of input. here: 2-D data
+// ConvNetJS works on 3-Dimensional volumes (sx, sy, depth), but if you're not dealing with images
+// then the first two dimensions (sx, sy) will always be kept at size 1
+layer_defs.push({type:'input', out_sx:1, out_sy:1, out_depth:2});
+// declare 20 neurons, followed by ReLU (rectified linear unit non-linearity)
+layer_defs.push({type:'fc', num_neurons:20, activation:'relu'}); 
+// declare the linear classifier on top of the previous hidden layer
+layer_defs.push({type:'softmax', num_classes:10});
+
+var net = new convnetjs.Net();
+net.makeLayers(layer_defs);
+
+// forward a random data point through the network
+var x = new convnetjs.Vol([0.3, -0.5]);
+var prob = net.forward(x); 
+
+// prob is a Vol. Vols have a field .w that stores the raw data, and .dw that stores gradients
+console.log('probability that x is class 0: ' + prob.w[0]); // prints 0.50101
+
+var trainer = new convnetjs.SGDTrainer(net, {learning_rate:0.01, l2_decay:0.001});
+trainer.train(x, 0); // train the network, specifying that x is class zero
+
+var prob2 = net.forward(x);
+console.log('probability that x is class 0: ' + prob2.w[0]);
+// now prints 0.50374, slightly higher than previous 0.50101: the networks
+// weights have been adjusted by the Trainer to give a higher probability to
+// the class we trained the network with (zero)
+```
+
+and here is a small **Convolutional Neural Network** if you wish to predict on images:
+
+```javascript
+var layer_defs = [];
+layer_defs.push({type:'input', out_sx:32, out_sy:32, out_depth:3}); // declare size of input
+// output Vol is of size 32x32x3 here
+layer_defs.push({type:'conv', sx:5, filters:16, stride:1, pad:2, activation:'relu'});
+// the layer will perform convolution with 16 kernels, each of size 5x5.
+// the input will be padded with 2 pixels on all sides to make the output Vol of the same size
+// output Vol will thus be 32x32x16 at this point
+layer_defs.push({type:'pool', sx:2, stride:2});
+// output Vol is of size 16x16x16 here
+layer_defs.push({type:'conv', sx:5, filters:20, stride:1, pad:2, activation:'relu'});
+// output Vol is of size 16x16x20 here
+layer_defs.push({type:'pool', sx:2, stride:2});
+// output Vol is of size 8x8x20 here
+layer_defs.push({type:'conv', sx:5, filters:20, stride:1, pad:2, activation:'relu'});
+// output Vol is of size 8x8x20 here
+layer_defs.push({type:'pool', sx:2, stride:2});
+// output Vol is of size 4x4x20 here
+layer_defs.push({type:'softmax', num_classes:10});
+// output Vol is of size 1x1x10 here
+
+net = new convnetjs.Net();
+net.makeLayers(layer_defs);
+
+// helpful utility for converting images into Vols is included
+var x = convnetjs.img_to_vol(document.getElementById('some_image'))
+var output_probabilities_vol = net.forward(x)
+```
+
+## Getting Started
+A [Getting Started](http://cs.stanford.edu/people/karpathy/convnetjs/started.html) tutorial is available on main page.
+
+The full [Documentation](http://cs.stanford.edu/people/karpathy/convnetjs/docs.html) can also be found there.
+
+See the **releases** page for this project to get the minified, compiled library, and a direct link to is also available below for convenience (but please host your own copy)
+
+- [convnet.js](http://cs.stanford.edu/people/karpathy/convnetjs/build/convnet.js)
+- [convnet-min.js](http://cs.stanford.edu/people/karpathy/convnetjs/build/convnet-min.js)
+
+## Compiling the library from src/ to build/
+If you would like to add features to the library, you will have to change the code in `src/` and then compile the library into the `build/` directory. The compilation script simply concatenates files in `src/` and then minifies the result.
+
+The compilation is done using an ant task: it compiles `build/convnet.js` by concatenating the source files in `src/` and then minifies the result into `build/convnet-min.js`. Make sure you have **ant** installed (on Ubuntu you can simply *sudo apt-get install* it), then cd into `compile/` directory and run:
+
+    $ ant -lib yuicompressor-2.4.8.jar -f build.xml
+
+The output files will be in `build/`
+## Use in Node
+The library is also available on *node.js*:
+
+1. Install it: `$ npm install convnetjs`
+2. Use it: `var convnetjs = require("convnetjs");`
+
+## License
+MIT