Mastering Generative AI Systems Engineering
Idioma: inglés
Editorial: Orange Education Pvt Ltd, 2026
- Tapa blanda
- Nuevo
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Vendedor de IberLibro desde 11 de junio de 1999
Condición: Nuevo
EUR 47,88
Cantidad disponible: Más de 20 disponibles
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N° de ref. del artículo L0-9789349887947
- Título
- Mastering Generative AI Systems Engineering
- Autor
- Kumar, Praveen
- Editorial
- Orange Education Pvt Ltd
- Año de publicación
- 2026
- Estado
- New
- Encuadernación
- PAP
- Idioma
- inglés
- ISBN 10
- 9349887940
- ISBN 13
- 9789349887947
- Peso del artículo
- 1366 gramos
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.
● Clear mathematical explanations that connect theory with practical model building.
● Real-world case studies across computer vision, NLP, data augmentation, and AI deployment.
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. The journey continues with LLMs and GPT-based systems, covering prompt engineering, RAG pipelines, LangChain applications, and agentic AI workflows. Thus, by the end, you will be ready to design and build powerful generative AI systems—from image generators and translation tools to intelligent assistants and custom LLM-powered applications.
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.
● Develop real-world AI solutions for image synthesis, NLP, and multimodal tasks.
● Evaluate, optimize, and deploy generative models for scalable production systems.
● Implement ethical, responsible, and safety-driven practices for generative AI development.
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
“Sinopsis” puede pertenecer a otra edición de este título.
PBShop.store UK
Fairford, GLOS, Reino Unido
Vendedor de IberLibro desde 11 de junio de 1999
Tarifas de envío de Reino Unido a Estados Unidos de America
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|---|---|---|
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