Description

Generative AI is rapidly transforming how organizations create content, build intelligent systems, and automate complex tasks. Understanding how these models work—and how to build them—is now a career-defining skill for developers and data professionals.

Mastering Generative AI Systems Engineering begins with the core foundations of generative AI. You will explore the essential mathematics, latent spaces, probability concepts, and neural network principles behind VAEs and GANs. The book then guides you through advanced systems such as CycleGANs, StyleGANs, and cutting-edge Diffusion Models—the engines behind today’s most powerful generative tools.

What you will learn
● Design, train, and fine-tune state-of-the-art GANs, VAEs, and diffusion models.
● Build powerful LLM and GPT-based applications using RAG, LangChain, and agentic workflows.
● Apply core mathematical concepts to understand and optimize generative architectures.

Who is This Book For?
This book is designed for machine learning engineers, data scientists, AI developers, NLP engineers, computer vision specialists, research scientists, and software engineers aiming to advance their expertise in generative AI. Readers should have the basic knowledge of Python, deep learning fundamentals, and familiarity with neural networks to fully benefit from the hands-on projects and real-world case studies.