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Autor(en): 
  • Praveen Kumar
  • Mastering Generative AI Systems Engineering: Design, Train, and Deploy Powerful Generative Models Across Vision, Language, and Multimodal AI Workflows 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Februar 2026  
    Genre:  Ratgeber 
     
    GANs (Generative Adversarial Networks) / Generative AI / Variational Autoencoders (VAEs)
    ISBN:  9789349887947 
    EAN-Code: 
    9789349887947 
    Verlag:  Orange Education Pvt Ltd 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 280 mm / B 216 mm / D 29 mm 
    Gewicht:  1366 gr 
    Seiten:  550 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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: ss-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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