An advanced UC Berkeley course on deep neural networks covering their design, visualization, and how they work, including generative models such as those behind Stable Diffusion. Lecture videos are reportedly available only to Berkeley students.

Commenters describe it as really good for advanced deep learning and point to its assignments as a way to learn implementation, alongside Stanford CS 231N and MIT 6.S191.

On Reddit (2)

1. Recommendsanon-mlr/MachineLearning2023in “[D] Are there any books that would help with implementing the ML/Deep Leaning algorithms?”

   Look at existing DL courses and complete their assignments. CS 231N at Stanford, CS 182 at Berkeley (and CS 198-126 if you are particularly interested in computer vision), CS 6.S191 at MIT, etc. are all great.

2. Recommendsseriger/learnmachinelearning2022in “Hi, what are the advanced courses/books in machine learning and neural nets? And where do I find them?”

   https://cs182sp21.github.io taught by Levine at UC Berkeley. The most recent iteration of the course is being taught by another professor. It covers generative models that help you understand ideas behind Stable Diffusion and is really good, but the lecture youtube videos are only available to…

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