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20 hours of new lectures on Deep Learning and Reinforcement Learning with lots of examples
If anyone's interested in a Deep Learning and Reinforcement Learning series, I forwarded the list from R. Compared to other lectures, I think this gives quite a broad/compact overview of the fields with lots of minimal examples to build on. Here are the links:
Deep Learning (playlist)
The first five lectures are more theoretical, the second half is more applied.
Lecture 2: Mathematical principles and backpropagation. (slides, colab, video)
Lecture 3: PyTorch programming: coding session. (colab1, colab2, video) - minor issues with audio, but it fixes itself later.
Lecture 5: Generative models. (slides, desmos, colab, video)
Lecture 6: Adversarial models. (slides, colab1, colab2, colab3, colab4, video)
Lecture 8: Sequential models: by u/samb-t. (slides, colab1, colab2, video)
Lecture 9: Flow models and implicit networks. (slides, SIREN, GON, video)
Lecture 10: Meta and manifold learning. (slides, interview, video)
Reinforcement Learning (playlist)
This is based on David Silver's course but targeting younger students within a shorter 50min format (missing the advanced derivations) + more examples and Colab code.
Lecture 2: Markov decision processes. (slides, colab, video)
Lecture 3: OpenAI gym. (video)
Lecture 6: Temporal-difference methods. (slides, colab, video)
Lecture 8: Policy gradient methods. (slides, code, theory, video)
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