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Top Books on Large Language Models (LLMs)

Top Books on Large Language Models (LLMs)
Image generated with DALL-E

TL;DR: This blog post recommends the top books on Large Language Models (LLMs). The books cover a wide range, from foundational concepts to practical applications and even building your own LLMs. Whether you’re a developer or just curious about this rapidly evolving field, there’s a book to help you on your journey.

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 Large Language Models (LLMs) is rapidly evolving. With new breakthroughs and applications emerging daily, it can be challenging to stay updated on the latest advancements. Books offer a structured and in-depth approach to understanding these complex topics. In this blog post, we’ll explore some of the top books on LLMs, providing a brief overview, table of contents, and Amazon links for each.

Building LLMs: A Guide to Large Language Models in Production

Table of Contents 

  • LLM theory fundamentals
  • Simple to advanced LLM techniques and frameworks
  • Code projects with real-world applications

In essence, this book equips developers with the knowledge and tools to build, improve, and deploy LLMs for production use.

[ Grab your copy ]


Understanding Large Language Models: Learning Their Underlying Concepts and Technologies

Understanding Large Language Models: Learning Their Underlying Concepts and Technologies First Edition. This book will teach you the underlying concepts of large language models (LLMs), as well as the technologies associated with them.The book starts with an introduction to the rise of conversational AIs such as ChatGPT, and how they are related to the broader spectrum of large language models. From there, you will learn about natural language processing (NLP), its core concepts, and how it has led to the rise of LLMs. Next, you will gain insight into transformers and how their characteristics, such as self-attention, enhance the capabilities of language modeling, along with the unique capabilities of LLMs. The book concludes with an exploration of the architectures of various LLMs and the opportunities presented by their ever-increasing capabilities—as well as the dangers of their misuse.
Understanding Large Language Models: Learning Their Underlying Concepts and Technologies

TL;DR: This book provides a comprehensive introduction to LLMs, explaining their underlying concepts and technologies. It covers everything from the basics of NLP to the architecture of LLMs and their applications.

Table of Contents:
  • Introduction to Large Language Models
  • Natural Language Processing (NLP) Fundamentals
  • The Architecture of Large Language Models
  • Training and Fine-tuning LLMs
  • Applications of Large Language Models
  • Ethical Considerations

In essence, this book serves as a foundational guide to understanding LLMs. It demystifies complex concepts, making them accessible to a wide audience. From the basics of NLP to the intricate architecture of LLMs, this book provides a solid groundwork for those looking to delve deeper into the world of large language models.

[ Grab your copy ]


Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications

Table of Contents:

  • Understanding LLMs and Their Capabilities
  • Identifying Suitable Use Cases
  • Data Preparation and Model Selection
  • Building and Deploying LLM-Based Applications
  • Optimizing Costs and Performance
  • Measuring and Improving LLM Performance

In essence, this book equips readers with the practical knowledge to transform LLMs into real-world, cost-effective solutions. By focusing on use case identification, data optimization, and model deployment, it bridges the gap between theoretical understanding and tangible business value.

[ Grab your copy ]


Hands-On Generative AI with Transformers and Diffusion Models. Learn how to use generative media techniques with AI to create novel images or music in this practical, hands-on guide. Data scientists and software engineers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to your needs, and how to combine existing building blocks to create new models and creative applications in different domains.This book introduces theoretical concepts in an intuitive way, with extensive code samples and illustrations that you can run on services such as Google Colaboratory, Kaggle, or Hugging Face Spaces with minimal setup. You'll learn how to use open source libraries such as Transformers and Diffusers, conduct code exploration, and study several existing projects to help guide your work.
Hands-On Generative AI with Transformers and Diffusion Models

TL;DR: This hands-on guide delves into the world of generative AI, covering both transformers and diffusion models. Readers will learn how to create various forms of generative media, from images to music.

Table of Contents:

  • Introduction to Generative AI
  • Understanding Transformers
  • Building Transformer-Based Models
  • Introduction to Diffusion Models
  • Creating Generative Models with Diffusion
  • Advanced Topics and Applications

In essence, this book transforms abstract concepts into practical skills, guiding readers through the creation of stunning and innovative generative media.

[ Grab your copy ]


Prompt Engineering for Generative AI

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

TL;DR: This book focuses on the art of prompt engineering, essential for effectively interacting with LLMs and generative AI models. It provides practical guidance on crafting prompts to achieve desired outcomes.

Table of Contents:

  • Introduction to Prompt Engineering
  • Understanding LLMs and Their Capabilities
  • Crafting Effective Prompts
  • Fine-tuning Prompts for Specific Tasks
  • Advanced Prompt Engineering Techniques
  • Ethical Considerations in Prompt Engineering

In essence, this book equips readers with the skills to effectively communicate with AI models, transforming vague requests into precise instructions. It’s a valuable resource for anyone looking to harness the full potential of large language models.

[ Grab your copy ]


Deep Generative Modeling

Image of Deep Generative Modeling on Amazon. This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions.
Deep Generative Modeling on Amazon

TL;DR: This book explores the theoretical foundations of generative models, combining probabilistic modeling with deep learning. It covers various generative model architectures and their applications.

Table of Contents:

  • Introduction to Generative Modeling
  • Probabilistic Graphical Models
  • Variational Autoencoders
  • Generative Adversarial Networks
  • Normalizing Flows
  • Applications of Generative Models

In essence, this book provides a deep dive into the theoretical underpinnings of generative models. It bridges the gap between probabilistic modeling and deep learning, exploring various architectures like Variational Autoencoders and Generative Adversarial Networks. Readers will gain a solid understanding of how these models work and their potential applications.

[ Grab your copy ]


Build a Large Language Model (From Scratch)

TL;DR: For those interested in building LLMs from the ground up, this book provides a step-by-step guide. It covers the technical aspects of LLM development, including architecture, training, and deployment.

Table of Contents:

  • Introduction to Large Language Model Architecture
  • Data Collection and Preprocessing
  • Model Training and Optimization
  • Evaluation and Fine-tuning
  • Deployment and Scaling

In essence, this book is a hands-on guide for aspiring LLM developers. It walks readers through the entire process of building a language model from the ground up, covering everything from data preparation to model deployment.

[ Grab your copy ]


Designing Large Language Model Applications

TL;DR: This book focuses on the practical side of LLMs, guiding readers on building real-world applications. It covers the process from idea to deployment, emphasizing user experience and business value.

Table of Contents:

  • Understanding LLM Capabilities and Limitations
  • Identifying Suitable Application Domains
  • Designing User-Centric LLM Applications
  • Developing and Testing LLM-Based Products
  • Deployment and Scaling Considerations
  • Measuring and Improving LLM Application Performance

In essence, this book bridges the gap between LLM theory and practical application. It offers a structured approach to building successful LLM-powered products, emphasizing user needs, business objectives, and technical implementation.

[ Grab your copy ]


Reinforcement Learning for Human-in-the-Loop Systems

Image of the Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI book. Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively.Summary Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process.
Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI on Amazon

TL;DR: This book explores the intersection of reinforcement learning and human interaction, focusing on developing AI systems that collaborate effectively with humans. It’s particularly relevant for understanding how LLMs can be improved through human feedback.

Table of Contents:

  • Introduction to Reinforcement Learning
  • Human-in-the-Loop Reinforcement Learning
  • Designing Effective Human-AI Interactions
  • Applications in Language Models and Beyond
  • Ethical Considerations

In essence, this book bridges the gap between human intelligence and machine learning, exploring how to create AI systems that learn and adapt through collaboration with humans. It offers valuable insights into building AI models that are not only intelligent but also aligned with human values and goals.

[ Grab your copy ]


The Alignment Problem: Machine Learning and Human Values

Image of The Alignment Problem: Machine Learning and Human Values Audible Logo Audible Audiobook – Unabridged book. A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them.Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us - and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem.
The Alignment Problem: Machine Learning and Human Values Audible Logo Audible Audiobook – Unabridged on Amazon.

Table of Contents:

  • Understanding the Alignment Problem
  • AI Safety and Control
  • Value Learning and Moral Decision Making
  • Societal Implications of AI Alignment
  • Future Directions

In essence, this book delves into the critical question of how to ensure AI systems, especially LLMs, align with human values. It explores the potential dangers of misaligned AI and offers insights into developing safeguards to prevent harmful outcomes.

[ Grab your copy ]


Generative AI for Business: How to Implement Generative AI to Transform Your Organization

Hyperautomation with Generative AI: Learn how Hyperautomation and Generative AI can help you transform your business and create new value (English Edition)
Learn how Hyperautomation and Generative AI can help you transform your business and create new value

TL;DR: This book provides practical guidance on leveraging generative AI, including LLMs, to drive business growth and innovation. It covers various applications and strategies for successful implementation.

Table of Contents:

  • Understanding Generative AI and Its Potential
  • Assessing Business Opportunities for Generative AI
  • Building a Generative AI Strategy
  • Implementing Generative AI Solutions
  • Measuring and Optimizing Generative AI Impact

In essence, this book translates the potential of generative AI into actionable business strategies. It equips readers with the tools to identify opportunities, build effective AI solutions, and measure their impact on the bottom line.

[ Grab your copy ]


In an era where artificial intelligence is reshaping industries and pushing the boundaries of what’s possible, understanding the capabilities and applications of large language models (LLMs) is crucial. By exploring the top books on large language models (LLMs), individuals can gain deeper insights into these technologies, from foundational theories to practical implementations. Whether you’re a seasoned developer or just embarking on your AI journey, these books serve as valuable resources, offering structured guidance and diverse perspectives that can enhance your knowledge and skills.

As AI continues to evolve, staying informed and educated is vital. The recommended books provide an excellent starting point for anyone looking to delve into the world of LLMs and generative AI. By engaging with these resources, readers can better understand how these models function, their potential applications, and the ethical considerations involved. This list is not exhaustive and represents a selection of popular and highly-rated books on large language models. It’s essential to consider your specific interests and learning goals when choosing a book. This knowledge is not only beneficial for personal growth but also essential for contributing to the development of AI technologies that align with human values and needs. Embrace the opportunity to expand your understanding through these top books, and be prepared to navigate the exciting challenges and opportunities that lie ahead in the field of 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.


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.

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