Learn Python Generative AI

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

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.

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.

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

Zonunfeli Ralte, a seasoned professional with a Master’s in Business Administration and Economics, boasts 15 years of experience in Analytics, Finance, and AI. Currently, she is the CEO and Founder of RastrAI, while also serving as a Principal AI Consultant, developing GenAI applications for diverse industries. Zonufeli has an impressive academic contribution with 6 IEEE research papers including Large Language Models (LLM), Deep learning and computer vision, 3 of which received best paper awards. Her multifaceted expertise and leadership make her a notable figure in the AI community. Additionally, she has filed 1 patent in GenAI.

Indrajit Kar, a master’s graduate in Computational Biology from Bengaluru, also holds a Bachelor’s in Science from the same institution with more than two decades of experience in AI and ML. He is an experienced intrapreneur, having built AI teams at Siemens, Accenture, IBM, and Infinite Data Systems. Presently, he is the AVP and Global Head of AI and ML leading AI research (ZAIR) and Data Practices. Indrajit has published 22 research papers across IEEE, Springers, Wiley Online Library, and CRC press, covering topics like LLM, Computer Vision, NLP, and more. He has 14 patents, including Generative AI. He is a mentor for startups and a recipient of multiple awards, including the 40 Under 40 Data Scientists award. He is also author of 2 AI books.