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
SHARK is a versatile and high-performance open-source library for machine learning, developed in C++. It encompasses a variety of techniques, including both linear and nonlinear optimization, kernel methods, neural networks, and more. This library serves as an essential resource for both practical applications and academic research endeavors. Built on top of Boost and CMake, SHARK is designed to be cross-platform, supporting operating systems such as Windows, Solaris, MacOS X, and Linux. It operates under the flexible GNU Lesser General Public License, allowing for broad usage and distribution. With a strong balance between flexibility, user-friendliness, and computational performance, SHARK includes a wide array of algorithms from diverse fields of machine learning and computational intelligence, facilitating easy integration and extension. Moreover, it boasts unique algorithms that, to the best of our knowledge, are not available in any other competing frameworks. This makes SHARK a particularly valuable tool for developers and researchers alike.
API Access
Has API
API Access
Has API
Integrations
Qwen3-Omni
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
SHARK
Founded
2018
Website
image.diku.dk/shark/sphinx_pages/build/html/index.html
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization