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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 Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Qwen3-Omni Yes 

Integrations

Qwen3-Omni No 

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

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

m0n0wall

Website

m0n0.ch/wall/index.php

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 

Product Features

Firewall

Alerts / Notifications No 
Application Visibility / Control No 
Automated Testing No 
Intrusion Prevention No 
LDAP Integration No 
Physical / Virtual Environment No 
Sandbox / Threat Simulation No 
Threat Identification No 

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