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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)
•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
PAT Rating™
Editor Rating
Aggregated User Rating
Rate Here
Ease of use
8.9
6.4
Features & Functionality
9.0
10
Advanced Features
8.8
8.0
Integration
8.8
10
Performance
9.0
8.0
Customer Support
7.6
10
Implementation
6.0
Renew & Recommend
3.0
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.
8.9
Editor Rating
7.7
Aggregated User Rating
5 ratings
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).
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