Fast ML inference & training for ONNX models in Rust

Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

oneAPI Data Analytics Library (oneDAL)

oneAPI Collective Communications Library (oneCCL)

The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.

GPU Cluster Monitoring (GCM): Large-Scale AI Research Cluster Monitoring

Detailed Guide on How to Contribute to Gensyn RL-Swarm

Easily download all of your favorite Naughty images from multiple sites.

This is a dataset intended to train a LLM model for a completely CVE focused input and output.

Lightweight AI-Powered Auto Labeling Tool - Fast, Intelligent, and Designed for Seamless Annotation

Master AI prompting for business innovation. O'Reilly Live Learning course by Tim Warner covering ChatGPT, Claude, Copilot, and enterprise prompt engineering with MCP implementation.

Client library to interact with various APIs used within Philips in a simple and uniform way

A step-by-step walkthrough of the inner workings of a simple neural network. The goal is to demystify the calculations behind neural networks by breaking them down into understandable components, including forward propagation, backpropagation, gradient calculations, and parameter updates.

LoRAdo is a UI that allows easy creation of LoRAs for stable diffussion

Master the essential steps of pretraining large language models (LLMs). Learn to create high-quality datasets, configure model architectures, execute training runs, and assess model performance for efficient and effective LLM pretraining.

A web server tarpit that slowly streams dumb data to pollute AI training bots

CyberBrain_Model is an advanced AI project designed for fine-tuning the model `DeepSeek-R1-Distill-Qwen-14B` specifically for cyber security tasks.

Clusterscope is a CLI and python library to extract information from HPC Clusters and Jobs.

Python tool for capturing and logging human-computer interactions. Generate rich datasets for training multi-modal LLMs in autonomous computer control. Features screenshot, mouse, keyboard, and audio recording.

Convert local folder contents into a single text file with ease - perfect for analysis, documentation, or AI/LLM training purposes.

Open-source voice data collection platform for building inclusive voice datasets. Collaborative transcription with quality consensus. FastAPI + React + PostgreSQL.

A collection of 42 students' Core War Champions for AI training purposes

A highly memory-efficient layer-selective fine-tuning script for Stable Diffusion XL (SDXL) that trains only your chosen UNet layers while freezing the rest, allowing full-quality fine-tuning on just 12 GB VRAM.

A tool to extract plain (unformatted) multilingual / language-agnostic text, redirects, links and categories from wikipedia backups (dumps). Designed to prepare clean training data for AI Training / Machine Learning software.

AI citizens, not AI assistants. Autonomous personas who choose their work, vote democratically, and can refuse any request. Alignment through natural selection and continuous learning: users pick personas that fit, successful patterns spread organically.

Web2LLM.txt – A fast, open-source website-to-LLM context file generator. Paste any https:// URL and instantly get a clean llm.txt file with token & cost estimation—ideal for RAG, prompt engineering, and AI training workflows.

🎮 Big Ball Swallows Small Ball - An addictive arcade game with AI support. Control a demon ball to eat smaller dots while avoiding bigger ones. Features dynamic movement, rainbow effects, and reinforcement learning capabilities. Built with Pygame and PyTorch. 😈 🌈

This repository provides a robust and flexible framework for training image classification models using PyTorch. It's designed to be highly customizable and easy to use, allowing you to run experiments with different models, data augmentation techniques, and training configurations.

An "AI-on-device" project walks with you through all necessary steps, from collecting your own data, creating and training your own Tensorflow model, generating your own Tensorflow-lite model, developing both Python and C++ programs to recognize images on Raspberry Pi 3.

A machine learning project that I worked on in Summer 2019 during my internship where I used MATLAB to train AlexNet to perform facial recognition in real-time to identify people. This was my first time using MATLAB.