Mastering Generative AI Systems Engineering - Design, Train, and Deploy Powerful Generative Models Across Vision, Language, and Multimodal AI Workflows (English Edition) #952782

Create, Imagine, and Innovate with the Power of Generative AI

Key Features
● Get a free one-month digital subscription to www.avaskillshelf.com
● Comprehensive coverage of generative models—from VAEs and GANs to Diffusion and LLMs.
● Hands-on projects using PyTorch, TensorFlow, LangChain, and modern AI toolchains.

Book 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.

Table of Contents
1. Introduction to Generative Models
2. Mathematical Foundations
3. Introduction to Variational Autoencoders
4. Introduction to Generative Adversarial Networks
5. Deep Convolutional GANs
6. Conditional Generative Adversarial Networks
7. Cycle GANs
8. Style GANs
9. Variational Autoencoders Revisited: β-VAE and CVAE
10. Diffusion Models
11. Data Augmentation with Generative Models
12. Generative Models in Natural Language Processing
13. Model Evaluation and Optimization
14. Deployment of Generative Models
15. Ethical Considerations and Future Directions
16. Introduction to Large Language Models
17. Generative Pre-Trained Transformers
18. Langchain: Building AI-Powered Applications
19. Prompt Engineering, RAG, and Fine-Tuning
20. Advanced Concepts
21. Best Practices for Generative Models
       Index