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1. Finally an amazing generative AI book
   Description:Another awesome book from ByteByteGo! I am absolutely enjoying reading every chapter of this book.Good:The content flows well and is easy to follow.Topics are explained clearly with helpful diagrams and examples.Lots of important topics are in one place.Bad:Some sections could benefit from more detailed explanations.Could include test-time scaling and related algorithms (e.g., star, rest, verifiers, search) for a more complete overview.Overall, I think this book is by far the best resource for learning about GenAI. It made it much easier for me to learn the technical details of such systems, which I had been struggling with for a while.

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2. An exceptional book for transitioning to GenAI roles
   I’m a software engineer at FAANG, but I work in a different area. I’ve been trying to switch to ML and GenAI roles, but it’s been hard to find good resources. There are so many blogs, videos, and difficult papers, and it’s hard to know which ones are actually important.This book is helping me learn fundemental GenAI topics. What I like most about the book is how it explains difficult topics in a way that’s easy to understand. For me, it’s by far the most useful book I’ve read. Highly recommend it!

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3. A well-written guide to learn GenAI
   In my opinion, this book maintains a great balance between system design and ML theories. Some of the key topics that are really well-covered are: distributed training, decoder-only and encoder-decoder transformers, LLMs, pre-training and post-training, RLHF, RoPE, ViT, RAG, GANs, VQ-VAE, U-Net, DiT, DreamBooth, Textual Inversion, LoRA, LDM.

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