Mastering Generative AI Systems Engineering - Design Train and Deploy Powerful Generative Models Across Vision Language and Multimodal AI Workflows
Praveen Kumar
Editorial: Orange Education Pvt Ltd
Sinopsis
Create, Imagine, and Innovate with the Power of Generative AIKey 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 DescriptionGenerative 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 Contents1. Introduction to Generative Models2. Mathematical Foundations3. Introduction to Variational Autoencoders4. Introduction to Generative Adversarial Networks5. Deep Convolutional GANs6. Conditional Generative Adversarial Networks7. Cycle GANs8. Style GANs9. Variational Autoencoders Revisited: β-VAE and CVAE10. Diffusion Models11. Data Augmentation with Generative Models12. Generative Models in Natural Language Processing13. Model Evaluation and Optimization14. Deployment of Generative Models15. Ethical Considerations and Future Directions16. Introduction to Large Language Models17. Generative Pre-Trained Transformers18. Langchain: Building AI-Powered Applications19. Prompt Engineering, RAG, and Fine-Tuning20. Advanced Concepts21. Best Practices for Generative Models Index
Disponible desde: 25/02/2026.
Longitud de impresión: 550 páginas.
