This book researches the intricate world of generative Artificial Intelligence, offering readers an extensive understanding of various components and applications in this field. The book begins with an in-depth analysis of generative models, providing a solid foundation and exploring their combination nuances. It then focuses on enhancing TransVAE, a variational autoencoder, and introduces the Swin Transformer in generative AI. The inclusion of cutting edge applications like building an image search using Pinecone and a vector database further enriches its content. The narrative shifts to practical applications, showcasing GenAI's impact in healthcare, retail, and finance, with real-world examples and innovative solutions. In the healthcare sector, it emphasizes AI's transformative role in diagnostics and patient care. In retail and finance, it illustrates how AI revolutionizes customer engagement and decision making. The book concludes by synthesizing key learnings, offering insights into the future of generative AI, and making it a comprehensive guide for diverse industries. Readers will find themselves equipped with a profound understanding of generative AI, its current applications, and its boundless potential for future innovations.

• Cover
• Title Page
• Copyright Page
• Dedication Page
• About the Authors
• About the Reviewers
• Acknowledgements
• Preface
• Table of Contents
• 1. Introducing Generative AI
  • Introduction
  • Structure
  • Objectives
  • Overview of generative models
  • Discriminative vs. generative models
  • Types of discriminative and generative models
  • Strengths and weaknesses
    • Class imbalance scenario
    • Generative modeling framework
      • Sample Space
      • Probability density function
      • Maximum likelihood
      • KL divergence
      • GMM code using TensorFlow probability
  • Conclusion
• 2. Designing Generative Adversarial Networks
  • Introduction
  • Structure
  • Objectives
  • Generative Adversarial Networks
  • Types of GANs available
  • Architecture of a GAN
    • Equation
    • Discriminator loss
    • Generator loss
  • Vanilla GAN
    • Outline crucial factors in GAN architecture design
    • Major challenges in designing GANs architecture
  • Architecture of Deep Convolutional GANs
  • Architecture of Wasserstein GANs
  • Architecture of Conditional GANs
  • Architecture of CycleGANs
  • Architecture of progressive GANs
  • Architecture of StyleGANs
  • Architecture of Pix2Pix
  • Conclusion
  • Multiple choice questions
    • Answers
• 3. Training and Developing Generative Adversarial Networks
  • Introduction
  • Structure
  • Objectives
  • Generative Adversarial Training
  • Generating MNIST data: Basic GAN implementation
  • Issues during training a GANs
    • Mode collapse
    • Vanishing gradients
    • Oscillation
    • Unstability
    • Evaluation
  • Case study: Common practical implementation of GANs for augmentation and balancing classes
  • Conclusion
• 4. Architecting Auto Encoder for Generative AI
  • Introduction
  • Structure
  • Objectives
  • Auto Encoders
    • Regularization
    • Creating a bottleneck
  • Key distinctions with autoencoders
    • Autoencoders
    • GANs
  • Importance of regularization in auto encoders
  • Cifar10
  • Anomaly detection using auto encoder
  • Autoencoders with convolutional layers
    • Architecture
    • Capturing spatial information
    • CNN versus ANN Autoencoders
  • Conclusion
• 5. Building and Training Generative Autoencoders
  • Introduction
  • Structure
  • Objectives
  • Latent space
  • Difference between GANs latent space and AE latent space
  • Key distinctions with autoencoders latent space
  • Adding color to a grayscale image using autoencoders
  • Coding advanced auto encoders
    • Multi modal auto encoders
  • Loss in autoencoders
    • Mean squared error loss
    • Binary cross-entropy loss
    • Categorical cross-entropy loss
    • Kullback-leibler divergence loss
    • Huber loss
  • Challenges in training auto encoders and mitigation
  • AE vs. VAE
  • Conclusion
• 6. Designing Generative Variation Auto Encoder
  • Introduction
  • Structure
  • Objectives
  • Story of VAE
  • VAE vs AE
    • Math behind the latent space
      • Deterministic Autoencoder
      • Stochastic Variational Autoencoder
  • Key distinctions with autoencoder latent space
    • Can the VAE Latent space be stochastic as well as deterministic
    • Dirichlet distribution
  • Importance of the latent space when designing a VAE
  • Vanilla VAE architecture
    • The ELBO
    • The reparameterization trick
  • Challenges in Vanilla VAE
  • Types of VAE
  • Conclusion
• 7. Building Variational Autoencoders for Generative AI
  • Introduction
  • Structure
  • Objectives
  • Key focus areas in VAE research
  • Building a VAE with Dirichlet distribution: Non-CNN Approach
  • Building a VAE with Dirichlet distribution: CNN Approach
    • Difference between two networks
  • VAE with Non Dirichlet distribution
  • KL divergence
  • Common loss function sin VAE
  • Common issues and possible solutions while training VAE
  • Missing data handling during generation
  • Optimization techniques
  • Conclusion
• 8. Fundamental of Designing New Age Generative Vision Transformer
  • Introduction
  • Structure
  • Objectives
  • The evolution
    • The birth of transformers
    • Overview of transformer architectures
    • Applications in NLP
    • Generative transformers and language modeling
    • Transformer in computer vision
  • Difference between VAE, GANs, and Transformers
    • Transformers
    • Generative Adversarial Networks
    • Variational autoencoders
    • Differences and applications
  • Vision Transformer
  • Understanding self-attention
  • NLP vs vision
    • NLP transformer
      • Self-attention mechanism
      • Feed-forward neural networks
    • Vision transformer
      • Patch embeddings
      • Positional embeddings
      • Transformer encoder
  • Architectural attention
    • Dot product attention
    • Scaled dot product attention
    • Additive attention
    • Multi-head attention
    • Cross attention
      • Compute attention scores
      • Compute cross-attention output
  • When to use which architectural attention
  • Functional attention
    • Hard attention
      • Equation: Sampling-based hard attention
    • Soft attention
      • Equation: Soft attention
    • Global attention
      • Equation: Global attention
    • Local attention
      • Equation: local attention
  • When to use which functional attention
    • Hard attention
    • Soft attention
    • Global attention
    • Local attention
  • Conclusion
• 9. Implementing Generative Vision Transformer
  • Introduction
  • Structure
  • Objectives
  • STL dataset
    • Key features of the STL-10 dataset
  • Developing a VAE model on STL dataset
  • Implementation of VAE architecture with TensorFlow
    • Outputs
  • Pytorch
  • Transition from VAE to Generative Transformer Model: Keras Vit Library
  • Implementing a ViT model from scratch
    • Outputs
  • Implementing a ViT model pre trained with ViT model
    • Outputs
  • Training Pretrained ViT vs ViT scratch
    • Pretrained Vision Transformer
      • Advantages
      • Disadvantages
    • Training a VIT model from scratch
      • Advantages
      • Disadvantages
  • Examining the loss curve
  • Optimization of ViT models
  • Conclusion
• 10. Architectural Refactoring for Generative Modeling
  • Introduction
  • Structure
  • Objectives
  • STL dataset
  • Exploring the combination process: Outline
  • Refactoring TransVAE and improving
    • Cyclic Learning Rate Schedule
      • LearningRateScheduler
      • EarlyStopping
      • Weight decay: L2 regularization
  • Improved Encoder Decoder
  • SWIN-Transformer
  • Implementation of SWIN Transformer: VAE
  • Improving the models
  • Conclusion
• 11. Major Technical Roadblocks in Generative AI and Way Forward
  • Introduction
  • Structure
  • Objectives
  • Challenges and hurdles in Generative AI
    • NLP based generative models
  • Large language models and image-based foundation models
  • Embedding in language models
  • Embedding in image
  • Generative AI and embeddings
  • Vector data bases and image embeddings
    • Vector databases
    • Image embeddings
  • Building an image search using pinecone and vector database
  • Conclusion
• 12. Overview and Application of Generative AI Models
  • Introduction
  • Structure
  • Objectives
  • GenAI in hospital
  • GenAI in dental
  • GenAI in radiology
  • GenAI in retail
  • GenAI in finance
  • GenAI in corporate finance
  • GenAI in insurance
  • Conclusion
• 13. Key Learnings
  • Introduction
  • Structure
  • Objectives
  • 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