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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

m0n0wall is an initiative focused on developing a comprehensive, embedded firewall software solution that, when paired with an embedded PC, delivers all essential features found in commercial firewall devices, including user-friendliness, at a significantly lower cost, being free software. This project utilizes a minimal version of FreeBSD, incorporating a web server, PHP, and several other utilities, with the entire system's configuration maintained in a single XML text file to ensure clarity and simplicity. Notably, m0n0wall is likely the first UNIX-based system to implement its boot-time configuration using PHP instead of the traditional shell scripts, and it uniquely stores all system configurations in XML format, showcasing an innovative approach in firewall technology. This distinct method enhances both usability and manageability, marking a significant advancement in the realm of open-source firewall solutions.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Qwen3-Omni

Integrations

Qwen3-Omni

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
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

m0n0wall

Website

m0n0.ch/wall/index.php

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Product Features

Firewall

Alerts / Notifications
Application Visibility / Control
Automated Testing
Intrusion Prevention
LDAP Integration
Physical / Virtual Environment
Sandbox / Threat Simulation
Threat Identification

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Alternatives

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