Applied Generative AI for Beginners

Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs

Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada

• Publisher: Apress
• Year: 2023
• ISBN:
  9781484299944
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI.

Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You'll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains.

Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights.

What You Will Learn

• Gain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google Bard
• Implement large language models using Sklearn, complete with code examples and best practices for real-world application
• Learn how to integrate LLM's in enterprises, including aspects like LLMOps and technology stack selection
• Understand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insights

Who This Book Is For

Data scientists, AI practitioners, Researchers and software engineers interested in generative AI and LLMs.

Table of contents

• Cover
• Front Matter
• 1. Introduction to Generative AI
• 2. Evolution of Neural Networks to Large Language Models
• 3. LLMs and Transformers
• 4. The ChatGPT Architecture: An In-Depth Exploration of OpenAI’s Conversational Language Model
• 5. Google Bard and Beyond
• 6. Implement LLMs Using Sklearn
• 7. LLMs for Enterprise and LLMOps
• 8. Diffusion Model and Generative AI for Images
• 9. ChatGPT Use Cases
• Back Matter