Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications

This book does a great job covering generative AI from the ground up. Unlike many resources out there that show you how to call APIs, Babcock and Bali actually walk you through what's happening under the hood. The progression makes sense - you start with the basics (neural networks, probability) and work your way up to the current stuff everyone's talking about: LLMs, prompt engineering, and diffusion models. I particularly appreciated the chapters on optimizing these models for real use (LoRA, quantization) since that's where the rubber meets the road in production. The code examples are solid and use PyTorch with Hugging Face, which is pretty much the standard stack right now. They actually run without needing a supercomputer, which is refreshing. You get hands-on with everything from VAEs and GANs for images to building RAG systems with LangChain. A couple of things I liked: the treatment of open-source models like Llama and Mixtral is really useful for deciding what to use, and they don't shy away from the ethical stuff (deepfakes, adversarial attacks). You'll need decent Python skills and some ML background to get the most out of it, but if you've got that, this is a solid investment. It's comprehensive without being overwhelming, and more importantly, it gives you the foundation to understand what comes next in this fast-moving field. Worth it for anyone building real applications with generative AI.

Generative AI with Python and PyTorch by Joseph Babcock and Raghav Bali published by Packt is an awesome book for anyone wanting to learn generative AI. It’s super easy to follow and covers everything from text to images, using cool tools like GPT-4 and LangChain. I loved how it mixes clear explanations with real examples you can try. The authors know their stuff and make AI exciting to learn. Whether you’re building smart systems or playing with AI art, this book has it all. It’s perfect for data scientists or coders who want to grow their skills. Chapters cover generative models, neural networks, text generation with LSTMs and transformers, LLMs like LLaMA, prompt engineering, and image creation using GANs and VAEs. From deepfakes to AI art, it’s a clear, hands-on journey for building cutting-edge AI applications with PyTorch. Highly recommended for its engaging style and real-world applications.

I'm still getting into AI and GenAI seems to be one of the hotter parts of AI. After opening this book, I found it's packed full of into of how I can best use GenAI in my work. This is a must-read for those who are getting into GenAI.

This is a great book, with solid background on GenAI concepts. If you are a beginner this book will guide you to master GenAI. If you are not, this book will dive you into complex concepts and the historical evolution of GenAI. Really recommended.

This book has good info on Gen AI. LLMs are all anyone is talking about along with other AI methods. This book is a good primer about all kinds of gen ai models. You won't be an expert, but you will learn enough to get the idea.

The book is really good for beginners as there are ton of resources online and this book tries to bring it all up at one place. I myself is new to GenAI so this book will personally help me to know more about it and make me efficient to use the knowledge in the book in my real world projects.

I started off my career with Gans love to see so many improvements with the GAN space I love the in depth detailed diagrams in the book, how why and where these have been implemented, really well written book and a must buy cheers!!!