Popular GitHub Repositories for Ml Security

An Open Source Machine Learning Framework for Everyone

YOLOv5 ? in PyTorch > ONNX > CoreML > TFLite

100 Days of ML Coding

Learn how to design, develop, deploy and iterate on production-grade ML applications.

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Opiniated RAG for integrating GenAI in your apps ? Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.

Visualizer for neural network, deep learning and machine learning models

Cross-platform, customizable ML solutions for live and streaming media.

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. ?Docker-friendly.?Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf

This repository is primarily maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), artificial intelligence security, vulnerability research, exploit development, reverse engineering, and more.

? A ranked list of awesome machine learning Python libraries. Updated weekly.

Open source platform for the machine learning lifecycle

Universal LLM Deployment Engine with ML Compilation

? The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

Open standard for machine learning interoperability

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

A WebGL accelerated JavaScript library for training and deploying ML models.

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

VIP cheatsheets for Stanford's CS 229 Machine Learning