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
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
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