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ConvNetJS
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ConvNetJS

Overview
Synopsis

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.

Category

Artificial Neural Network Software

Features

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.

License

Proprietary Software

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Website
Company

ConvNetJS

Rating
Our Rating
User Rating
Ease of use
7.6
Features & Functionality
7.6
Advanced Features
7.6
Integration
7.6
Customer Support
7.6
Performance
7.6
Training
Implementation
Renew & Recommend
Bottom Line

The library allows you to formulate and solve Neural Networks in Javascript. Current support includes 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 and An experimental Reinforcement Learning module, based on Deep Q Learning.

7.6
Our Rating
User Rating
You have rated this

ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in users’ browsers. Users just open a tab and they are training. No software requirements, no compilers, no installations, no GPUs, no sweat. The library allows users to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (a PhD student at Stanford). However, the library has since been extended by contributions from the community. The code is available on Github under MIT license. Pull requests for new features / layers / demos and miscellaneous improvements are encouraged. The library is also available on npm for use in Nodejs, under name convnetjs. There are two ways to use the library: inside the browser, or on a server using node.js. The fastest way to obtain the library in a plug-and-play way for users who don’t care about developing is through a link to convnet-min.js, which contains the minified library. Alternatively, users can also choose to download the latest release of the library from Github. The file that they would probably be most interested in is build/convnet-min.js, which contains the entire library. And to use it users must create a bare-bones index.html file in some folder and copy build/convnet-min.js to the same folder. The entire library is based around transforming 3-dimensional volumes of numbers. These volumes are stored in the Vol class, which is at the heart of the library. The Vol class is a wrapper around a 1-dimensional list of numbers (the activations, in field .w), their gradients (field .dw) and lastly contains three dimensions (fields .sx, .sy, .depth).

ConvNetJS

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Ease of use
Features & Functionality
Advanced Features
Integration
Customer Support
Performance
Training
Implementation
Renew & Recommend

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