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Description
Apache Yetus comprises a suite of libraries and tools designed to facilitate the contribution and release workflows for software projects. It offers a comprehensive framework for automatically validating new contributions against a range of standards recognized by the community, alongside features for documenting a clearly defined supported interface for downstream projects. Additionally, it equips release managers with tools to create release documentation based on data sourced from community issue trackers and source code repositories. Predominantly, the software is developed using shell and various scripting languages, with the project's name derived from a term linked to the Cymbium genus of gastropods, paying homage to shell code. The Yetus Precommit build, patch, and continuous integration suite empowers projects to formalize their criteria for patch acceptance and assess incoming contributions before they reach the review stage by a committer. Furthermore, the Audience Annotations feature enables developers to utilize Java Annotations to indicate which segments of their Java library are intended for public consumption, enhancing clarity for users. This combination of tools and features makes Yetus an invaluable resource for software development communities looking to streamline their processes.
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.
API Access
Has API
API Access
Has API
Integrations
Azure Pipelines
GitHub
GitLab
Jenkins
Jira
Qwen3-Omni
Semaphore
Travis CI
iTop VPN
Integrations
Azure Pipelines
GitHub
GitLab
Jenkins
Jira
Qwen3-Omni
Semaphore
Travis CI
iTop VPN
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
Apache Software Foundation
Founded
1999
Country
United States
Website
yetus.apache.org
Vendor Details
Company Name
ConvNetJS
Website
cs.stanford.edu/people/karpathy/convnetjs/
Product Features
Software Testing
Automated Testing
Black-Box Testing
Dynamic Testing
Issue Tracking
Manual Testing
Quality Assurance Planning
Reporting / Analytics
Static Testing
Test Case Management
Variable Testing Methods
White-Box Testing
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
