[N] 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 uploaded 20 hours of lectures on YouTube yesterday. 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 1: Introduction. (slides, video)
• 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 4: Designing models to generalise. (slides, video)
• Lecture 5: Generative models. (slides, desmos, colab, video)
• Lecture 6: Adversarial models. (slides, colab1, colab2, colab3, colab4, video)
• Lecture 7: Energy-based models. (slides, colab, 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 1: Foundations. (slides, video)
• Lecture 2: Markov decision processes. (slides, colab, video)
• Lecture 3: OpenAI gym. (video)
• Lecture 4: Dynamic programming. (slides, colab, video)
• Lecture 5: Monte Carlo methods. (slides, colab, video)
• Lecture 6: Temporal-difference methods. (slides, colab, video)
• Lecture 7: Function approximation. (slides, code, video)
• Lecture 8: Policy gradient methods. (slides, code, theory, video)
• Lecture 9: Model-based methods. (slides, video)
• Lecture 10: Extended methods. (slides, atari, video)