Mastering Generative AI Systems Engineering: Design, Train, and Deploy Powerful Generative Models Across Vision, Language, and Multimodal AI Workflows

Name: Mastering Generative AI Systems Engineering: Design, Train, and Deploy Powerful Generative Models Across Vision, Language, and Multimodal AI Workflows
Brand: Orange Education Pvt Ltd
SKU: 9789349887947
Price: 5385.0 INR
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by Praveen Kumar

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ISBN13: 9789349887947
Binding: Paperback
Subject: N/A
Publisher: Orange Education Pvt Ltd
Publisher Imprint: Orange Education Pvt Ltd
Publication Date: February 24, 2026
Pages: 550
Original Price: USD 49.95
Language: English
Edition: N/A
Item Weight: 1257 grams
BISAC Subject(s): Artificial Intelligence / Generative AI

About The Book

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

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.

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.

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

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