Reinforcement Learning for Gaming Full Python Course in 9 Hours
2.5
Youtube40,203 learners
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This 9-hour course is the perfect way to learn how to apply machine learning to gaming. It covers reinforcement learning tutorials for gaming using Python and Stable Baselines 3. You'll learn best practices for training reinforcement learning models for games, as well as how to preprocess environments, build RL models, run them live, and more. Plus, you'll get to practice on Mario, Doom, and Streetfighter. Connect with the instructor, Nick, on LinkedIn, Facebook, GitHub, and Patreon for support and discussion. Get ready to take your gaming to the next level with this comprehensive course!
Modern Reinforcement-learning using Deep Learning
1.4
Udemy2,771 learners
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Nitsan Soffair, a Deep RL researcher at BGU, is offering a course on the newest state-of-the-art Deep reinforcement-learning knowledge. In this course, you will learn about model types, algorithms and approaches, function approximation, deep reinforcement-learning, and deep multi-agent reinforcement-learning. You will also be able to validate your knowledge by answering short and very short quizzes of each lecture. The course can be completed in approximately two hours. Don't miss out on this opportunity to learn the latest in Deep RL!
Stanford CS25: V2 I Introduction to Transformers w& Andrej Karpathy
3.0
Youtube184,421 learners
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This CS25 I Stanford Seminar is a great opportunity to learn about the revolutionary technology of transformers. Led by Andrej Karpathy, the course will explore the details of how transformers work, and dive deep into the different kinds of transformers and how they're applied in different fields. From computer vision to reinforcement learning, GANs, speech, and even biology, transformers have enabled the creation of powerful language models like GPT-3 and were instrumental in DeepMind's AlphaFold2. Don't miss this chance to learn from the experts and explore the future of transformers. Click now to join the course and go forth and transform!
MIT 6S191: Reinforcement Learning
2.5
Youtube37,208 learners
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This MIT 6S191: Reinforcement Learning course provides an introduction to deep learning and its applications. Lecturer Alexander Amini will cover topics such as classes of learning problems, definitions, the Q function, deep Q networks, Atari results and limitations, policy learning algorithms, discrete vs continuous actions, training policy gradients, RL in real life, VISTA simulator, AlphaGo and AlphaZero and MuZero, and a summary. With all lectures, slides, and lab materials available online, this course is perfect for anyone interested in learning more about deep learning and its applications. Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!
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