Learn to unleash the power of AI creativity KEY FEATURES ● Understand the core concepts related to generative AI. ● Different types of generative models and their applications. ● Learn how to design generative AI neural networks using Python and TensorFlow. DESCRIPTION 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. WHAT YOU WILL LEARN ● Acquire practical skills in designing and implementing various generative AI models. ● Gain expertise in vector databases and image embeddings, crucial for image search and data retrieval. ● Navigate challenges in healthcare, retail, and finance using sector specific insights. ● Generate images and text with VAEs, GANs, LLMs, and vector databases. ● Focus on both traditional and cutting edge techniques in generative AI. WHO THIS BOOK IS FOR This book is for current and aspiring emerging AI deep learning professionals, architects, students, and anyone who is starting and learning a rewarding career in generative AI
Author(s): Zonunfeli Ralte, Indrajit Kar
Publisher: BPB Publications
Year: 2024
Language: English
Pages: 348
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
