GenAI is changing how we build software, and learning to master these models with Python is essential for any modern developer. This book is a comprehensive guide to the intricate world of GenAI.
This book serves as a complete guide to GenAI, covering foundational concepts and advanced system design. It begins with core principles of generative modeling, explaining probabilistic approaches and how they differ from traditional machine learning methods. The journey then moves into GANs, including their architecture, variants, training challenges, and evaluation techniques. Readers are introduced to VAEs, focusing on latent space representation, probabilistic learning, and practical design strategies. It then explores transformer architectures and Vision Transformers, explaining how attention mechanisms enable modern generative and multimodal systems. Hybrid approaches combining VAEs and transformers are also covered to demonstrate real-world model design.
By the end of this book, you will master the math and engineering behind GANs and VAEs to build high-quality visual synthesis networks. You will gain hands-on expertise in Vision Transformers, diffusion models, and scalable MLOps infrastructure using vector and graph databases.
WHAT YOU WILL LEARN
● Acquire practical skills in designing and implementing various generative AI models.
● Gain expertise in vector databases and image embeddings, crucial for image search and data retrieval.
● Generate images and text with VAEs, GANs, LLMs, and vector databases.
● Focus on both traditional and cutting-edge techniques in generative AI.
IN THIS EDITION, YOU WILL LEARN HOW TO:
● Master generative math to stabilize adversarial and stochastic vision networks.
● Build hybrid attention systems using transformers and diffusion architectures.
WHO THIS BOOK IS FOR
This book is designed for current and aspiring AI and deep learning professionals, architects, students, and anyone looking to begin a rewarding career in GenAI. It is ideal for those who want to build a strong foundation and progress from traditional generative models to modern, industry-grade GenAI systems and applications.
