Mastering generative systems engineering di kumar praveen (9 risultati)

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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

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Taschenbuch. Condizione: Neu. Mastering Generative AI Systems Engineering | Design, Train, and Deploy Powerful Generative Models Across Vision, Language, and Multimodal AI Workflows | Praveen Kumar | Taschenbuch | Englisch | 2026 | Orange Education Pvt Ltd | EAN 9789349887947 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Create, Imagine, and Innovate with the Power of Generative AIBook 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. 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 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: ß-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 ModelsIndex. …