pml-book VS diffusion-models-class

Una cunoscuta pe 'reinforced learning': https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf Doua foarte recente: https://github.com/probml/pml-book/releases/tag/2022-08-08 https://github.com/probml/pml2-book/releases/tag/2022-12-24

Kevin Murphy - Probabilistic Machine Learning

I don't know about this book, but there are August 2022 PDFs of "Probabilistic Machine Learning : An Introduction" and "Probabilistic Machine Learning : Advanced Topics" by Kevin Murphy available here: https://github.com/probml/pml-book

Is that recent enough?

Check these out: https://github.com/probml/pml-book

Diff from 2012 book, directly from the books website

"This version is being sent to MIT Press, and will appear in hard copy late 2021/ early 2022."

https://github.com/probml/pml-book/releases/tag/camera

If you want to have better interactions with the readers, then you can consider creating a GitHub repository for the book (e.g., https://github.com/probml/pml-book and https://github.com/mml-book/mml-book.github.io).

When you use stable diffusion, you can adjust the classifier free guidance scale to control how much it follows the input prompt. From what I understand(check https://github.com/huggingface/diffusion-models-class/tree/main/unit3), what cfg does is that it generates an unconditional image and an image conditional on the text prompt, and then scale up the difference.

https://huggingface.co/learn/nlp-course/ https://huggingface.co/docs/transformers (go through the task guide) https://github.com/huggingface/diffusion-models-class http://d2l.ai/ https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ

I couldn't understand the original paper(havent done meth in a long time). This blog post and short course help me to understand.

The Diffusion Models Course is another good resource to learn more technical details.

Next, you need to become familiar with the diffusion model. I recommend this huggingface's course(https://github.com/huggingface/diffusion-models-class) because it is very high quality and you will learn while using diffusers. At first glance, it may not seem directly related to this game, but in my case, knowing what is happening in diffusers helped me in many ways: trial and error, inspiration for ideas, etc. I had no knowledge of pytorch (the deep learning library used for diffusers), so I also took this course (https://www.udacity.com/course/deep-learning-pytorch--ud188) which was in the prerequisites for that huggingface's course. It was also very good.

Hugging Face Dreambooth Hackaton details

Details: https://github.com/huggingface/diffusion-models-class/blob/main/hackathon/README.md