Curriculum

23 Chapters · 44 Lessons

  1. 1

    Free Preview

    1. Free Preview Free preview
  2. 2

    Chapter 1: Introduction to Generative Models

    1. (Included in full purchase)

      Introduction to Generative Models

    2. (Included in full purchase)

      Introduction to Generative Models

  3. 3

    Chapter 2: Mathematical Foundations

    1. (Included in full purchase)

      Mathematical Foundations

    2. (Included in full purchase)

      Mathematical Foundations

  4. 4

    Chapter 3: Introduction to Variational Autoencoders

    1. (Included in full purchase)

      Introduction to Variational Autoencoders

    2. (Included in full purchase)

      Introduction to Variational Autoencoders

  5. 5

    Chapter 4: Introduction to Generative Adversarial Networks

    1. (Included in full purchase)

      Introduction to Generative Adversarial Networks

    2. (Included in full purchase)

      Introduction to Generative Adversarial Networks

  6. 6

    Chapter 5: Deep Convolutional GANs

    1. (Included in full purchase)

      Deep Convolutional GANs

    2. (Included in full purchase)

      Deep Convolutional GANs

  7. 7

    Chapter 6: Conditional Generative Adversarial Networks

    1. (Included in full purchase)

      Conditional Generative Adversarial Networks

    2. (Included in full purchase)

      Conditional Generative Adversarial Networks

  8. 8

    Chapter 7: Cycle GANs

    1. (Included in full purchase)

      Cycle GANs

    2. (Included in full purchase)

      Cycle GANs

  9. 9

    Chapter 8: Style GANs

    1. (Included in full purchase)

      Style GANs

    2. (Included in full purchase)

      Style GANs

  10. 10

    Chapter 9: Variational Autoencoders Revisited: β-VAE and CVAE

    1. (Included in full purchase)

      Variational Autoencoders Revisited: β-VAE and CVAE

    2. (Included in full purchase)

      Variational Autoencoders Revisited: β-VAE and CVAE

  11. 11

    Chapter 10: Diffusion Models

    1. (Included in full purchase)

      Diffusion Models

    2. (Included in full purchase)

      Diffusion Models

  12. 12

    Chapter 11: Data Augmentation with Generative Models

    1. (Included in full purchase)

      Data Augmentation with Generative Models

    2. (Included in full purchase)

      Data Augmentation with Generative Models

  13. 13

    Chapter 12: Generative Models in Natural Language Processing

    1. (Included in full purchase)

      Generative Models in Natural Language Processing

    2. (Included in full purchase)

      Generative Models in Natural Language Processing

  14. 14

    Chapter 13: Model Evaluation and Optimization

    1. (Included in full purchase)

      Model Evaluation and Optimization

    2. (Included in full purchase)

      Model Evaluation and Optimization

  15. 15

    Chapter 14: Deployment of Generative Models

    1. (Included in full purchase)

      Deployment of Generative Models

    2. (Included in full purchase)

      Deployment of Generative Models

  16. 16

    Chapter 15: Ethical Considerations and Future Directions

    1. (Included in full purchase)

      Ethical Considerations and Future Directions

    2. (Included in full purchase)

      Ethical Considerations and Future Directions

  17. 17

    Chapter 16: Introduction to Large Language Models

    1. (Included in full purchase)

      Introduction to Large Language Models

    2. (Included in full purchase)

      Introduction to Large Language Models

  18. 18

    Chapter 17: Generative Pre-Trained Transformers

    1. (Included in full purchase)

      Generative Pre-Trained Transformers

    2. (Included in full purchase)

      Generative Pre-Trained Transformers

  19. 19

    Chapter 18: Langchain: Building AI-Powered Applications

    1. (Included in full purchase)

      Langchain: Building AI-Powered Applications

    2. (Included in full purchase)

      Langchain: Building AI-Powered Applications

  20. 20

    Chapter 19: Prompt Engineering, RAG, and Fine-Tuning

    1. (Included in full purchase)

      Prompt Engineering, RAG, and Fine-Tuning

    2. (Included in full purchase)

      Prompt Engineering, RAG, and Fine-Tuning

  21. 21

    Chapter 20: Advanced Concepts

    1. (Included in full purchase)

      Advanced Concepts

    2. (Included in full purchase)

      Advanced Concepts

  22. 22

    Chapter 21: Best Practices for Generative Models

    1. (Included in full purchase)

      Best Practices for Generative Models

    2. (Included in full purchase)

      Best Practices for Generative Models

  23. 23

    Index

    1. (Included in full purchase)

      Index

About the Course

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.

About the Author

Praveen Kumar is a global tech leader with more than 27 years in IT, having held CTO, VP, and architect roles across the USA, Canada, China, and Europe. An expert in AI, ML, Generative AI, Big Data, and cloud technologies, he has received state and national awards in software development and frequently delivers technology talks while mentoring developers worldwide.