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About

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.

About

From v1.0.0, the library is rewritten in Typescript and ES6 completely. The giant and old jQuery plugin doesn't exist anymore. With the new plugin-based architecture, the library has really small core. Everything else is built around as a plugin. Each file in the dist/css and dist/js folders has two versions: the normal code in the .css, .js files, and the minified code in the .min.css, .min.js files. In order to reduce the page loading time and enhance the user experience when visiting your site, you should use the .min.css, .min.js files in the production website. Meanwhile, in the development mode, you should use the normal files without .min part to debug the code more easy.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Developers, professionals and researchers seeking a solution for training deep learning models

Audience

Developers looking for a validation library for JavaScript

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Pricing

$50 one-time payment
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

ConvNetJS
cs.stanford.edu/people/karpathy/convnetjs/

Company Information

FormValidation
formvalidation.io

Alternatives

Alternatives

Deci

Deci

Deci AI

Categories

Deep Learning Supported
Neural Network Supported

Categories

Integrations

Bootstrap Not Supported
CSS Not Supported
CakePHP Not Supported
Google Chrome Not Supported
JavaScript Not Supported
Mozilla Firefox Not Supported
Qwen3-Omni Supported
Ruby on Rails Not Supported
Spring Framework Not Supported
Stripe Not Supported
TypeScript Not Supported
jQuery Not Supported

Integrations

Bootstrap Supported
CSS Supported
CakePHP Supported
Google Chrome Supported
JavaScript Supported
Mozilla Firefox Supported
Qwen3-Omni Not Supported
Ruby on Rails Supported
Spring Framework Supported
Stripe Supported
TypeScript Supported
jQuery Supported
Claim ConvNetJS and update features and information
Claim ConvNetJS and update features and information
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