Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

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

Dexie.js serves as a streamlined and dependable wrapper for IndexedDB, aimed at making client-side storage management more approachable. With a minified and gzipped size of around 29k, it presents a straightforward API that tackles the intricate challenges posed by the native IndexedDB, including inconsistent error management, inefficient querying, a lack of reactivity, and overall code complexity. The library is built upon a thoughtfully crafted API, featuring strong error handling, the ability to extend functionality, and awareness of change tracking, in addition to enhanced KeyRange capabilities for diverse operations such as case-insensitive searches, set matches, and OR conditions. By adhering to the IndexedDB specification and leveraging its complete feature set, Dexie.js allows developers to seamlessly interact with existing IndexedDB data without any requirement for data migration. Additionally, it supports real-time composable queries, permitting components to reflect database changes instantaneously across multiple front-end frameworks like React, Svelte, Vue, and Angular. Furthermore, with the integration of Dexie Cloud, developers can create reliable, authenticated, and access-controlled local-first applications with minimal additional coding effort. This combination of features makes Dexie.js a highly valuable tool for modern web development, particularly when managing client-side data efficiently.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Angular No 
IndexedDB No 
Qwen3-Omni Yes 
React No 
Svelte No 
Vue.js No 

Integrations

Angular Yes 
IndexedDB Yes 
Qwen3-Omni No 
React Yes 
Svelte Yes 
Vue.js Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

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 Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
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 Yes 

Vendor Details

Company Name

ConvNetJS

Website

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

Vendor Details

Company Name

Dexie

Founded

2014

Country

United States

Website

dexie.org

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 

Alternatives

Alternatives