#jupyter_notebook #control #course #course_materials #jupyter #jupyter_notebooks #latex #lecture #lecture_notes #machine_learning #online_learning #online_videos #open_education #open_education_resources #open_educational_resources #prediction #python #reinforcement_learning #teaching #teaching_materials #tutorial

https://github.com/upb-lea/reinforcement_learning_course_materials

#jupyter_notebook #deep_reinforcement_learning #reinforcement_learning #reinforcement_learning_excercises

https://github.com/huggingface/deep-rl-class

#jupyter_notebook #andrew_ng #andrew_ng_course #andrew_ng_machine_learning #andrewng #coursera #coursera_machine_learning #data_science #deep_learning #deep_neural_networks #dl #machine_learning #ml #neural_network #neural_networks #numpy #pandas #python #pytorch #reinforcement_learning

https://github.com/ashishpatel26/Andrew-NG-Notes

#jupyter_notebook #computer_vision #course #deep_learning #machine_learning #materials #natural_language_processing #python #pytorch #reinforcement_learning #seminars

https://github.com/girafe-ai/ml-course

#other #computer_vision #deep_learning #embeddings #machine_learning #nlp #recommender_system #reinforcement_learning #survey

https://github.com/eugeneyan/ml-surveys

#python #artificial_intelligence #attention_mechanisms #deep_learning #human_feedback #reinforcement_learning #transformers

https://github.com/lucidrains/PaLM-rlhf-pytorch

#python #jax #monte_carlo_tree_search #reinforcement_learning

https://github.com/deepmind/mctx

#jupyter_notebook #finance #fintech #investment_strategies #jupyter_notebook #machine_learning #python #pytorch #quantitative_trading #reinforcement_learning #stock_market #trading_platform

https://github.com/TradeMaster-NTU/TradeMaster

#python #deep_reinforcement_learning #gym #hyperparameter_optimization #hyperparameter_search #hyperparameter_tuning #lab #openai #optimization #pybullet #pybullet_environments #pytorch #reinforcement_learning #rl #robotics #sde #stable_baselines #tuning_hyperparameters

https://github.com/DLR-RM/rl-baselines3-zoo

#python #ai #computer_vision #deep_learning #deep_reinforcement_learning #reinforcement_learning #research #robotics #sim2real #simulator

https://github.com/facebookresearch/habitat-lab

#python #a2c #actor_critic #advantage_actor_critic #ale #atari #deep_learning #deep_reinforcement_learning #gym #machine_learning #phasic_policy_gradient #ppo #proximal_policy_optimization #pytorch #reinforcement_learning #wandb

https://github.com/vwxyzjn/cleanrl

#python #api #gym #reinforcement_learning

https://github.com/Farama-Foundation/Gymnasium

#python #ai #control #decision_making #distributed_computing #machine_learning #marl #model_based_reinforcement_learning #multi_agent_reinforcement_learning #pytorch #reinforcement_learning #rl #robotics #torch

https://github.com/pytorch/rl

#python #artificial_intelligence #atari #deep_learning #diffusion_models #machine_learning #reinforcement_learning #research #world_models

DIAMOND is a new way to train AI agents using a technique called diffusion in world models. It allows the agent to learn and play games like Atari and even simulate environments like Counter-Strike: Global Offensive. The benefit to you is that you can easily try out these pre-trained models on your own computer by following simple installation steps. You can watch the AI play, take control yourself, and even adjust how the AI imagines the game world, making it a fun and interactive way to explore advanced AI technology.

https://github.com/eloialonso/diamond

#jupyter_notebook #aws #data_science #deep_learning #examples #inference #jupyter_notebook #machine_learning #mlops #reinforcement_learning #sagemaker #training

SageMaker-Core is a new Python SDK for Amazon SageMaker that makes it easier to work with machine learning resources. It provides an object-oriented interface, which means you can manage resources like training jobs, models, and endpoints more intuitively. The SDK simplifies code by allowing resource chaining, eliminating the need to manually specify parameters. It also includes features like auto code completion, comprehensive documentation, and type hints, making it faster and less error-prone to write code. This helps developers customize their ML workloads more efficiently and streamline their development process.

https://github.com/aws/amazon-sagemaker-examples