About the Book
Hands-on Generative AI with Transformers and Diffusion Models This book is a comprehensive guide for anyone who wants to understand, build, and deploy generative AI systems in real-world contexts. Written for engineers, data scientists, and AI practitioners, this book takes you from the core concepts of generative modeling all the way to advanced architectures and enterprise applications.
Key Highlights:
The foundations chapter introduces tokens, embeddings, and latent space, the building blocks of generative AI.
The transformer and attention mechanism chapter explains the core architecture that enabled LLMs and multimodal systems.
The section on diffusion models covers the noise-to-data process behind breakthroughs like Stable Diffusion and text-to-video.
In industry use cases, drug discovery and healthcare AI show how generative models drive molecule design and protein analysis.
The book closes with emerging research trends, giving engineers a roadmap into multimodality, reasoning agents, and alignment challenges.
The book begins with the foundations of generative AI, introducing tokens, embeddings, and latent spaces, along with essential mathematical tools like probability, distributions, and optimization. From there, readers move into neural network fundamentals before diving into the architectures that revolutionized AI - autoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs), transformers, and diffusion models. Each architecture is explained in detail with Python code examples, giving readers hands-on experience with implementation. In the middle sections, the book covers large language models (LLMs), including pretraining, fine-tuning, tokenization, and alignment strategies such as reinforcement learning with human feedback (RLHF). It also explores multimodal generative AI, where text, images, audio, and video converge in state-of-the-art models like CLIP, Flamingo, Gemini, and GPT-4. The scaling laws and infrastructure chapter guides readers through efficient training, GPU/TPU clusters, model parallelism, and cost optimization, making this book highly practical for engineers building at scale.
Part IV moves into industry applications and case studies, showcasing how generative AI powers chatbots, virtual assistants, content generation, AI art and design, code generation (e.g., GitHub Copilot), and synthetic data for computer vision and medical imaging. It also demonstrates groundbreaking use cases in drug discovery, healthcare AI, finance, and risk modeling, showing how generative models are accelerating innovation across industries.
The final part of the book addresses ethics, safety, and the future of generative AI. Topics include bias in generative systems, responsible use, AI governance, and adversarial risks like deepfakes and model jailbreaking. Engineers will also gain insight into emerging research trends such as multimodal agents, reasoning systems, continual learning, privacy-preserving AI, and the debate between open-source vs. proprietary models. Perspectives on Artificial General Intelligence (AGI) and alignment research round out the book, giving readers a forward-looking roadmap.
Packed with clear explanations, detailed diagrams (described in-text), and runnable Python code, this book equips generative AI engineers with both the theoretical foundations and the practical skills to innovate responsibly. Whether you're building LLMs, diffusion models, multimodal systems, or enterprise-grade generative AI applications, this guide will accelerate your journey.
