Popular GitHub Repositories for Clean Energy

Minimal and clean examples of machine learning algorithms implementations

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

Label, clean and enrich text datasets with LLMs.

A deep learning package for many-body potential energy representation and molecular dynamics

All-in-one productivity app and AI assistant with Tasks, Notes, Calendar, Diary and Bookmarks.

Clean PyTorch implementations of imitation and reward learning algorithms

Extract clean data from anywhere, powered by vision-language models ?

"Deep Generative Modeling": Introductory Examples

A practical architecture styles for migrating from legacy monolith to modern cloud native application with the latest technologies and architectures like Vertical Slice Architecture, Event Sourcing, CQRS, DDD, gRpc, MongoDB, RabbitMq, and Masstransit in .Net 9.

(CVPR 2021 Oral) Open World Object Detection

Stable Diffusion AI client app for Android

? A practical and imaginary food delivery microservices, built with .Net 8, MassTransit, Domain-Driven Design, CQRS, Vertical Slice Architecture, Event-Driven Architecture, and the latest technologies.

Boltzmann Machines in TensorFlow with examples

List of Molecular and Material design using Generative AI and Deep Learning

Power Tools for AI Engineers With Deadlines

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

???♂? Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach.