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Description
ConvNetJS is a JavaScript library designed for training deep learning models, specifically neural networks, directly in your web browser. With just a simple tab open, you can start the training process without needing any software installations, compilers, or even GPUs—it's that hassle-free. The library enables users to create and implement neural networks using JavaScript and was initially developed by @karpathy, but it has since been enhanced through community contributions, which are greatly encouraged. For those who want a quick and easy way to access the library without delving into development, you can download the minified version via the link to convnet-min.js. Alternatively, you can opt to get the latest version from GitHub, where the file you'll likely want is build/convnet-min.js, which includes the complete library. To get started, simply create a basic index.html file in a designated folder and place build/convnet-min.js in the same directory to begin experimenting with deep learning in your browser. This approach allows anyone, regardless of their technical background, to engage with neural networks effortlessly.
Description
Starting from version 1.0.0, the library has been entirely rewritten using Typescript and ES6, eliminating the outdated jQuery plugin. The new architecture is based on plugins, resulting in a streamlined core library. Each file found in the dist/css and dist/js directories is available in two formats: the standard versions with .css and .js extensions, and the optimized versions with .min.css and .min.js extensions. For improved page load times and a better user experience on your production website, it is recommended to implement the minified files. Conversely, during development, utilizing the standard files without the .min suffix facilitates easier debugging and code maintenance. This transition not only enhances performance but also modernizes the overall structure of the library.
API Access
Has API
API Access
Has API
Integrations
Bootstrap
CSS
CakePHP
Google Chrome
JavaScript
Mozilla Firefox
Qwen3-Omni
Ruby on Rails
Spring Framework
Stripe
Integrations
Bootstrap
CSS
CakePHP
Google Chrome
JavaScript
Mozilla Firefox
Qwen3-Omni
Ruby on Rails
Spring Framework
Stripe
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$50 one-time payment
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
ConvNetJS
Website
cs.stanford.edu/people/karpathy/convnetjs/
Vendor Details
Company Name
FormValidation
Website
formvalidation.io
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization