Learn Python Generative AI, Ralte Z., Kar I., 2024

Learn Python Generative AI, Ralte Z., Kar I., 2024.
    
   Learn Python Generative AI is an extensive and comprehensive guide that delves deep into the world of generative artificial intelligence. This book provides a thorough understanding of the various components and applications in this rapidly evolving field. It begins with a detailed analysis, laying a solid foundation for exploring generative models. The combination process of different generative models is discussed in depth, offering a roadmap to understand the complexities involved in integrating various AI models and techniques.




Overview of generative models.
Generative AI refers to a type of artificial intelligence that can generate new data or content, such as images, videos, or text, with similar characteristics to the training data it was given. Generative AI has progressed rapidly over the years, and much of this progress has been driven by advances in deep learning.

One of the earliest examples of generative AI was the autoencoder, developed in the 1980s. Autoencoders are neural networks that can learn to compress and reconstruct data, and they can also be used to generate new data by sampling from the known compressed representation. However, autoencoders have limitations regarding the types of data they can develop and the quality of the generated output.

Contents.
1. Introducing Generative AI.
2. Designing Generative Adversarial Networks.
3. Training and Developing Generative Adversarial Networks.
4. Architecting Auto Encoder for Generative AI.
5. Building and Training Generative Autoencoders.
6. Designing Generative Variation Auto Encoder.
7. Building Variational Autoencoders for Generative AI.
8. Fundamental of Designing New Age Generative Vision Transformer.
9. Implementing Generative Vision Transformer.
10. Architectural Refactoring for Generative Modeling.
11. Major Technical Roadblocks in Generative AI and Way Forward.
12. Overview and Application of Generative AI Models.
13. Key Learnings.
Key learning from all the chapters.
Chapter 1: Introducing Generative AI.
Chapter 2: Designing Generative Adversarial Networks.
Chapter 3: Training and Developing Generative Adversarial Networks.
Chapter 4: Architecting Auto Encoder for Generative AI.
Chapter 5: Building and Training Generative Autoencoders.
Chapter 6: Designing Generative VAE.
Chapter 7: Building Variational AutoEncoders for Generative AI.
Chapter 8: Designing New Age Generative Vision Transformer for Generative Learning.
Chapter 9: Implementing Generative Vision Transformers.
Chapter 10: Architectural Refactoring Combining Encoder-decoder and Transformers for Generative Modeling.
Chapter 11: Major Technical Roadblocks in Generative AI.
Chapter 12: Overview of Applications of Generative AI Models.
Index.



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