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Generative AI

Best Books on Generative AI

By Roberto Iriondo
0
112
8 min.
Best Books on Generative AI
Image generated with DALL-E

TL;DR: This blog post explores the top books on generative AI, covering a wide range of topics from foundational concepts to practical applications. Whether you’re a developer or enthusiast, these books offer valuable insights into the world of generative AI. The recommended books include practical guides to building generative AI models, understanding their ethical implications, and leveraging them for business applications.

Disclaimer: This post has been created with the help of generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us.

Introduction

The field of Generative AI is rapidly evolving, with new breakthroughs and applications emerging daily. To stay updated on the latest advancements, books offer a structured and in-depth approach to understanding these complex topics. This blog post will explore some of the top books on Generative AI, providing a brief overview, table of contents, and Amazon links for each.

Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play

Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play 2nd Edition by David Foster (Author) 4.6 4.6 out of 5 stars 116 ratings 4.3 on Goodreads 205 ratings See all formats and editions Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.

TL;DR: Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play is a comprehensive guide to building and training generative AI models using TensorFlow and Keras. It covers a wide range of techniques, from VAEs and GANs to Transformers and diffusion models. The book is ideal for machine learning engineers and data scientists looking to explore the exciting world of generative AI.

Table of Contents:

  • Part I: Introduction to Generative Deep Learning
    • What is Generative Deep Learning?
    • A Brief History of Generative Models
    • Deep Learning Fundamentals
  • Part II: Variational Autoencoders (VAEs)
    • Understanding VAEs
    • Building and Training VAEs
    • Applications of VAEs
  • Part III: Generative Adversarial Networks (GANs)
    • Understanding GANs
    • Building and Training GANs
    • Applications of GANs
  • Part IV: Transformers
    • Understanding Transformers
    • Building and Training Transformers
    • Applications of Transformers
  • Part V: Normalizing Flows
    • Understanding Normalizing Flows
    • Building and Training Normalizing Flows
    • Applications of Normalizing Flows
  • Part VI: Energy-Based Models
    • Understanding Energy-Based Models
    • Building and Training Energy-Based Models
    • Applications of Energy-Based Models
  • Part VII: Denoising Diffusion Models
    • Understanding Denoising Diffusion Models
    • Building and Training Denoising Diffusion Models
    • Applications of Denoising Diffusion Models
  • Part VIII: The Future of Generative AI
    • Trends and Predictions
    • Ethical Considerations
    • Opportunities and Challenges

[ Grab your copy ]

Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs 1st Edition

Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs 1st Edition by James Phoenix (Author), Mike Taylor (Author) 4.4 4.4 out of 5 stars 16 ratings See all formats and editions Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code

TL;DR: Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs is a practical guide to effectively interacting with Large Language Models (LLMs) and diffusion models. The book covers the principles of prompt engineering, which can help you coax reliable results from these models. It’s essential reading for developers looking to harness the power of generative AI.

Table of Contents:

  • Understanding the Interaction Chain
    • The Role of Prompts
    • The Structure of the Interaction Chain
  • Transforming Problems into Document Completion
    • Problem Formulation
    • Prompt Design
  • Leveraging LLM and Diffusion Model Architecture
    • Model Selection
    • Prompt Optimization
  • Prompt Engineering in Practice
    • Natural Language Processing
    • Text and Image Generation
    • Code Generation

[Grab your copy]

Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications 1st Edition

Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications 1st Edition by Chris Fregly (Author), Antje Barth (Author), Shelbee Eigenbrode (Author) 4.6 4.6 out of 5 stars     54 ratings 4.5 on Goodreads 40 ratings See all formats and editions Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock

TL;DR: Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications is a practical guide to implementing generative AI in your business. It covers the entire project life cycle, from use case definition to deployment. The book explores different types of generative AI models, including LLMs and multimodal models, and provides guidance on techniques like prompt engineering, fine-tuning, and reinforcement learning.

Table of Contents:

  • Understanding Generative AI
    • What is Generative AI?
    • Types of Generative AI Models
  • The Generative AI Project Lifecycle
    • Use Case Definition
    • Model Selection
    • Model Fine-tuning
    • Retrieval-Augmented Generation
    • Reinforcement Learning from Human Feedback  
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    • Model Quantization, Optimization, and Deployment
  • Building Context-Aware Applications
    • Prompt Engineering
    • In-Context Learning
    • LLMs and Multimodal Models
  • Leveraging AWS Services
    • Amazon Bedrock
    • SageMaker
    • Other Relevant Services
  • Case Studies and Best Practices
    • Real-world Examples
    • Tips and Tricks

[Grab your copy]

Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs by Ben Auffarth (Author) 4.2 4.2 out of 5 stars 60 ratings 3.4 on Goodreads 23 ratings See all formats and editions Get to grips with the LangChain framework from theory to deployment and develop production-ready applications. Code examples regularly updated on GitHub to keep you abreast of the latest LangChain developments. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to leverage LLMs' capabilities and work around their inherent weaknesses Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book Description ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis - illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity. What you will learn Understand LLMs, their strengths and limitations Grasp generative AI fundamentals and industry trends Create LLM apps with LangChain like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is for The book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena. Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily. Table of Contents What Is Generative AI? LangChain for LLM Apps Getting Started with LangChain Building Capable Assistants Building a Chatbot like ChatGPT Developing Software with Generative AI LLMs for Data Science Customizing LLMs and Their Output Generative AI in Production The Future of Generative Models

TL;DR: Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs is a practical guide to building LLM applications using the LangChain framework. It covers the fundamentals of LLMs, prompt engineering, fine-tuning, and deployment. The book is ideal for developers looking to harness the power of generative AI.

Table of Contents:

  • Understanding LLMs
    • What are LLMs?
    • Strengths and Limitations
  • Generative AI Fundamentals
    • Key Concepts
    • Industry Trends
  • Getting Started with LangChain
    • Installation and Setup
    • Basic Usage
  • Building Capable Assistants
    • Question-Answering Systems
    • Chatbots
  • Developing Software with Generative AI
    • Code Generation
    • Data Analysis
  • Customizing LLMs
    • Prompt Engineering
    • Fine-tuning
  • Generative AI in Production
    • Deployment Strategies
    • Evaluation
  • The Future of Generative Models
    • Trends and Predictions

[Grab your copy]

Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models by Joseph Babcock (Author), Raghav Bali (Author) 4.3 4.3 out of 5 stars 52 ratings See all formats and editions Fun and exciting projects to learn what artificial minds can create Key Features: Code examples are in TensorFlow 2, which make it easy for PyTorch users to follow along Look inside the most famous deep generative models, from GPT to MuseGAN Learn to build and adapt your own models in TensorFlow 2.x Explore exciting, cutting-edge use cases for deep generative AI Book Description: Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you'll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You'll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There's been an explosion in potential use cases for generative models. You'll look at Open AI's news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that's under the hood and uncover surprising links between text, image, and music generation. What You Will Learn: Export the code from GitHub into Google Colab to see how everything works for yourself Compose music using LSTM models, simple GANs, and MuseGAN Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN Learn how attention and transformers have changed NLP Build several text generation pipelines based on LSTMs, BERT, and GPT-2 Implement paired and unpaired style transfer with networks like StyleGAN Discover emerging applications of generative AI like folding proteins and creating videos from images Who this book is for: This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.

TL;DR: Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models is a practical guide to building generative AI models using TensorFlow 2. It covers a wide range of techniques and applications, from text generation to image manipulation. The book is ideal for Python programmers interested in exploring the creative possibilities of generative AI.

Table of Contents:

  • Introduction to Generative AI
    • What is Generative AI?
    • History of Generative Models
  • Deep Learning Fundamentals
    • TensorFlow 2 Basics
    • Neural Networks
  • Variational Autoencoders (VAEs)
    • Understanding VAEs
    • Building VAEs with TensorFlow
  • Generative Adversarial Networks (GANs)
    • Understanding GANs
    • Building GANs with TensorFlow
  • Long Short-Term Memory (LSTM) Networks
    • Understanding LSTMs
    • Building LSTMs with TensorFlow
  • Transformer Models
    • Understanding Transformers
    • Building Transformers with TensorFlow
  • Applications of Generative AI
    • Text Generation
    • Image Generation
    • Music Generation
    • Other Applications

[Grab your copy]

Introduction to Generative AI by Numa Dhamani (Author), Maggie Engler (Author) 4.8 4.8 out of 5 stars 9 ratings 3.7 on Goodreads 25 ratings See all formats and editions Generative AI tools like ChatGPT are amazing—but how will their use impact our society? This book introduces the world-transforming technology and the strategies you need to use generative AI safely and effectively. Introduction to Generative AI gives you the hows-and-whys of generative AI in accessible language. In this easy-to-read introduction, you’ll learn: How large language models (LLMs) work How to integrate generative AI into your personal and professional workflows Balancing innovation and responsibility The social, legal, and policy landscape around generative AI Societal impacts of generative AI Where AI is going Anyone who uses ChatGPT for even a few minutes can tell that it’s truly different from other chatbots or question-and-answer tools. Introduction to Generative AI guides you from that first eye-opening interaction to how these powerful tools can transform your personal and professional life. In it, you’ll get no-nonsense guidance on generative AI fundamentals to help you understand what these models are (and aren’t) capable of, and how you can use them to your greatest advantage. Foreword by Sahar Massachi. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Generative AI tools like ChatGPT, Bing, and Bard have permanently transformed the way we work, learn, and communicate. This delightful book shows you exactly how Generative AI works in plain, jargon-free English, along with the insights you’ll need to use it safely and effectively. About the book Introduction to Generative AI guides you through benefits, risks, and limitations of Generative AI technology. You’ll discover how AI models learn and think, explore best practices for creating text and graphics, and consider the impact of AI on society, the economy, and the law. Along the way, you’ll practice strategies for getting accurate responses and even understand how to handle misuse and security threats. What's inside How large language models work Integrate Generative AI into your daily work Balance innovation and responsibility About the reader For anyone interested in Generative AI. No technical experience required. About the author Numa Dhamani is a natural language processing expert working at the intersection of technology and society. Maggie Engler is an engineer and researcher currently working on safety for large language models. The technical editor on this book was Maris Sekar. Table of Contents 1 Large language models: The power of AI Evolution of natural language processing 2 Training large language models 3 Data privacy and safety with LLMs 4 The evolution of created content 5 Misuse and adversarial attacks 6 Accelerating productivity: Machine-augmented work 7 Making social connections with chatbots 8 What’s next for AI and LLMs 9 Broadening the horizon: Exploratory topics in AI

TL;DR: Introduction to Generative AI is a comprehensive guide to understanding and using generative AI tools like ChatGPT. It covers the basics of LLMs, best practices for using these tools, and the ethical considerations surrounding their use. The book is ideal for anyone interested in learning more about generative AI.

Table of Contents:

  • Large Language Models
    • Evolution of NLP
    • Training LLMs
  • Data Privacy and Safety
    • Data Privacy Concerns
    • Mitigating Risks
  • Created Content
    • Impact on Content Creation
    • Ethical Implications
  • Misuse and Adversarial Attacks
    • Identifying and Preventing Misuse
  • Accelerating Productivity
    • Using AI for Work
  • Social Connections with Chatbots
    • Building Relationships with AI
  • The Future of AI
    • Trends and Predictions

[Grab your copy]

Generative AI for Cloud Solutions: Architect modern AI LLMs in secure, scalable, and ethical cloud environments

Generative AI for Cloud Solutions: Architect modern AI LLMs in secure, scalable, and ethical cloud environments

TL;DR: Generative AI for Cloud Solutions: Architect modern AI LLMs in secure, scalable, and ethical cloud environments is a comprehensive guide to implementing generative AI, specifically LLMs and ChatGPT, on cloud platforms. It covers topics like fine-tuning, prompt engineering, responsible AI, and scaling strategies. The book is ideal for cloud architects, data scientists, and AI researchers looking to harness the power of generative AI in the cloud.

Table of Contents:

  • Cloud Computing Meets Generative AI
    • Introduction to Cloud Computing
    • Introduction to Generative AI
  • NLP Evolution and Transformers
    • Understanding NLP
    • Transformers and LLMs
  • Fine-Tuning LLMs
    • Customizing LLMs for Specific Tasks
  • Retrieval-Augmented Generation (RAG)
    • Enhancing LLM Performance with External Information
  • Prompt Engineering
    • Crafting Effective Prompts
  • Developing LLM-Based Cloud Applications
    • Frameworks and Tools
    • LLMOps
  • Deploying ChatGPT in the Cloud
    • Architecture Design
    • Scaling Strategies
  • Security and Privacy
    • Protecting Data and Models
  • Responsible AI
    • Ethical Considerations
  • Future of Generative AI
    • Trends and Emerging Use Cases

[Grab your copy]

Generative AI: Navigating the Course to the Artificial General Intelligence Future 1st Edition

Generative AI: Navigating the Course to the Artificial General Intelligence Future 1st Edition by Martin Musiol (Author) 5.0 5.0 out of 5 stars 4 ratings See all formats and editions An engaging and essential discussion of generative artificial intelligence In Generative AI: Navigating the Course to the Artificial General Intelligence Future, celebrated author Martin Musiol―founder and CEO of generativeAI.net and GenAI Lead for Europe at Infosys―delivers an incisive and one-of-a-kind discussion of the current capabilities, future potential, and inner workings of generative artificial intelligence. In the book, you'll explore the short but eventful history of generative artificial intelligence, what it's achieved so far, and how it's likely to evolve in the future. You'll also get a peek at how emerging technologies are converging to create exciting new possibilities in the GenAI space. Musiol analyzes complex and foundational topics in generative AI, breaking them down into straightforward and easy-to-understand pieces. You'll also find: Bold predictions about the future emergence of Artificial General Intelligence via the merging of current AI models Fascinating explorations of the ethical implications of AI, its potential downsides, and the possible rewards Insightful commentary on Autonomous AI Agents and how AI assistants will become integral to daily life in professional and private contexts Perfect for anyone interested in the intersection of ethics, technology, business, and society―and for entrepreneurs looking to take advantage of this tech revolution―Generative AI offers an intuitive, comprehensive discussion of this fascinating new technology.

TL;DR: Generative AI: Navigating the Course to the Artificial General Intelligence Future is a comprehensive exploration of generative AI by Martin Musiol. It covers the history, current capabilities, and future potential of generative AI, including its role in the development of Artificial General Intelligence (AGI). The book also explores the ethical considerations and practical applications of AI assistants.

Table of Contents:

  • Introduction to Generative AI
    • What is Generative AI?
    • A Brief History of Generative Models
  • Understanding Generative AI Techniques
    • Variational Autoencoders (VAEs)
    • Generative Adversarial Networks (GANs)
    • Other Generative AI Models
  • The Road to Artificial General Intelligence
    • How Generative AI Contributes to AGI
    • Merging Current AI Models
  • The Ethical Landscape of AI
    • Potential Downsides of AI
    • Responsible Development of AI
  • Autonomous AI Agents and the Future of Work
    • How AI Assistants will Transform Our Lives
    • Applications in Different Fields

[Grab your copy]

The ChatGPT Millionaire: Making Money Online has never been this EASY (Chat GPT and Generative AI Mastery Series)

TL;DR: The ChatGPT Millionaire: Making Money Online has never been this EASY is a guide to using ChatGPT to generate income online. It covers topics like creating passive income sources, writing engaging content, and automating tasks. The book includes practical examples and a bonus ChatGPT prompt cheat sheet.

Table of Contents:

  • Introduction to ChatGPT
    • What is ChatGPT?
    • How Does it Work?
  • Making Money with ChatGPT
    • Creating Passive Income Sources
    • Writing Engaging Content
    • Automating Tasks
  • Effective ChatGPT Usage
    • Guidelines and Tips
  • ChatGPT Prompts
    • 150+ Prompts for Various Tasks
  • Limitations of ChatGPT
    • Understanding ChatGPT’s Capabilities

[Grab your copy]

The field of Generative AI is rapidly evolving, offering exciting possibilities for developers, artists, and anyone curious about the future of technology. This blog post has explored some of the best books on Generative AI, providing a roadmap to enhance your understanding of this transformative field.

Whether you’re a seasoned developer seeking to build generative models or a complete beginner intrigued by the potential of AI-powered content creation, these books cater to a wide range of audiences. From foundational concepts like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to practical applications in areas like text generation and image manipulation, these resources offer valuable insights.

Keep in mind: This list is not exhaustive, and there are many other excellent books available. Consider your specific interests and goals when choosing a book to delve deeper into the world of Generative AI.

Do you have a book suggestion to add to this list of the best books on large language models? Please send it to us. We’d love to hear from you.

Join us on this incredible generative AI journey and be a part of the revolution. Stay tuned for updates and insights on generative AI by following us on X or LinkedIn.

The Hundred-Page Machine Learning Book
$25.15 (as of September 18, 2024 18:54 GMT -04:00 - More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)
Generative Artificial Intelligence: Exploring the Power and Potential of Generative AI
$37.87 (as of September 18, 2024 23:37 GMT -04:00 - More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)
Natural Language Understanding with Python: Combine natural language technology, deep learning, and large language models to create human-like language comprehension in computer systems
$29.99 (as of September 19, 2024 00:32 GMT -04:00 - More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)

Disclaimer: The content on this website reflects the views of contributing authors and not necessarily those of Generative AI Lab. This site may contain sponsored content, affiliate links, and material created with generative AI. Thank you for your support.

Roberto Iriondo
Roberto Iriondohttps://www.robertoiriondo.com/
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