Average Ratings 0 Ratings
Average Ratings 0 Ratings
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Starting from version 1.0.0, the library has been entirely rewritten using Typescript and ES6, eliminating the outdated jQuery plugin.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
Yes
API Access
Has API
Yes
Integrations
Bootstrap
No
CSS
No
CakePHP
No
Google Chrome
No
JavaScript
No
Mozilla Firefox
No
Qwen3-Omni
Yes
Ruby on Rails
No
Spring Framework
No
Stripe
No
Integrations
Bootstrap
Yes
CSS
Yes
CakePHP
Yes
Google Chrome
Yes
JavaScript
Yes
Mozilla Firefox
Yes
Qwen3-Omni
No
Ruby on Rails
Yes
Spring Framework
Yes
Stripe
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$50 one-time payment
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
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
No
Document Classification
No
Image Segmentation
No
ML Algorithm Library
No
Model Training
No
Neural Network Modeling
No
Self-Learning
No
Visualization
No