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Sanseviero, O., Cuenca, P., Passos, A., & Whitaker, J. (2024). Hands-on generative AI with transformers and diffusion models. O’Reilly Media.
Read Chapter 2: Transformers
Read Chapter 3: Compressing and Representing
Read Chapter 4: Diffusion Models

This book provides a practical deep dive into the most powerful generative AI models in use today, including transformers and diffusion models. It’s a vital read for anyone seeking hands-on experience with state-of-the-art architectures that power tools like ChatGPT and DALL·E.

Liu, M. (2024). Learn generative AI with PyTorch. Manning Publications.
Read Chapter 3: Generative adversarial networks: Shape and number generation
Read Chapter 4: Image Generation with GANs
Read Chapter 7: Variational Autoencoders
Read Chapter 9: Implement Attention Transformer
Read Chapter 15: Diffusion & TextToImage Transformers

Focused on accessibility and real-world implementation, this book is ideal for learners who want to build generative AI models from scratch using PyTorch. It breaks down core concepts into manageable projects, making it easier to go from theory to working applications.

Babcock, J., & Bali, R. (2023). Generative AI with Python and PyTorch (2nd ed.). Packt Publishing.
Read Chapter 4: NLP 2.0: Using Transformers to Generate Text
Read Chapter 11: Neural Networks Using VAEs
Read Chapter 12: Image Generation with GANs

This updated edition covers not just the foundations of generative AI, but also real coding practices and model fine-tuning using Python and PyTorch. It’s especially useful for learners looking to transition from concept to production-level skills.

Dinu, J. (2024). Programming generative AI [Video book]. Pearson.

Perfect for visual learners, this video-based resource guides you through generative AI development with a structured, accessible format. It complements reading-based materials by providing real-time demonstrations of tools and code in action.