Joseph Babcock, Raghav Bali | Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications. Second Edition (2025) [PDF] [EN]

You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models.
Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI.
What you will learn
Grasp the core concepts behind large language models and their capabilities
Craft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputs
Understand how attention and transformers have changed NLP
Optimize your diffusion models by combining them with VAEs
Build text generation pipelines based on LSTMs and LLMs
Leverage the power of open-source LLMs, such as Llama and Mistral, for diverse applications

Table of Contents
Introduction to Generative AI: Drawing Data from Models
Building Blocks of Deep Neural Networks
The Rise of Methods for Text Generation
NLP 2.0: Using Transformers to Generate Text
LLM Foundations
Open-Source LLMs
Prompt Engineering
LLM Toolbox
LLM Optimization Techniques
Emerging Applications in Generative AI
Neural Networks Using VAEs
Image Generation with GANs
Style Transfer with GANs
Deepfakes with GANs
Diffusion Models and AI Art