YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

Ultralytics YOLO 🚀

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: …

What the f*ck Python? 😱

A toolkit for developing and comparing reinforcement learning algorithms.

A hyperparameter optimization framework

Advanced Python Mastery (course by @dabeaz)

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

An educational resource to help anyone learn deep reinforcement learning.

An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

FastF1 is a python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

Build reliable Gen AI solutions without overhead 🍕

Barbershop: GAN-based Image Compositing using Segmentation Masks (SIGGRAPH Asia 2021)

High-quality single-file implementations of SOTA Offline and Offline-to-Online RL algorithms: AWAC, BC, CQL, DT, EDAC, IQL, SAC-N, TD3+BC, LB-SAC, SPOT, Cal-QL, ReBRAC

Deep learning with spiking neural networks (SNNs) in PyTorch.

GEKKO Python for Machine Learning and Dynamic Optimization

Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code

SBX: Stable Baselines Jax (SB3 + Jax) RL algorithms

BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically ground…

A Python toolkit for Reservoir Computing and Echo State Network experimentation based on pyTorch. EchoTorch is the only Python module available to easily create Deep Reservoir Computing models.

an open high-performance Optical Character Recognition (OCR) toolkit

A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.

This repository contains a race simulation to determine a race strategy for motorsport circuit races. Race strategy in this context means the determination of pit stops.

Prioritized Experience Replay implementation with proportional prioritization

Pytorch implementation of various autoencoders (contractive, denoising, convolutional, randomized)

Reinforcement Learning tool for Network Slice Placement problems

A collection of solutions for some interviews problems on interviewbit and leetcode