AForge.NET vs. ConvNetJS vs. Python vs. Torch Comparison Chart

ConvNetJS is a Javascript library for training deep learning models (neural networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. The library allows you to formulate and solve neural networks in Javascript, and was originally written by @karpathy. However, the library has since been extended by contributions from the community and more are warmly welcome. The fastest way to obtain the library in a plug-and-play way if you don't care about developing is through this link to convnet-min.js, which contains the minified library. Alternatively, you can also choose to download the latest release of the library from Github. The file you are probably most interested in is build/convnet-min.js, which contains the entire library. To use it, create a bare-bones index.html file in some folder and copy build/convnet-min.js to the same folder.

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.