Top 7 Free LLM Books (100% Trustworthy Links) : Chris
by: Chris
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I spent the last couple of hours scouring the web for free LLM books that are not trash, scam, or outright malicious links. This curated list is the proud result. Have fun reading! 
What Is ChatGPT Doing … and Why Does It Work? by Stephen Wolfram
Description: “Nobody expected this—not even its creators: ChatGPT has burst onto the scene as an AI capable of writing at a convincingly human level. But how does it really work? What’s going on inside its “AI mind”? In this short book, prominent scientist and computation pioneer Stephen Wolfram provides a readable and engaging explanation that draws on his decades-long unique experience at the frontiers of science and technology. Find out how the success of ChatGPT brings together the latest neural net technology with foundational questions about language and human thought posed by Aristotle more than two thousand years ago.”
Read the book for free here (HTML)
Large Language Models at Work by Vlad Riscutia
Description: “This book is aimed at software engineers wanting to learn about how they can integrate large language models into their software systems. It covers all the necessary domain concepts and comes with simple code samples. No prior AI knowledge required to understand this book, just basic programming. After reading the book, one should have a solid understanding of all the required pieces to build a large language model-powered solution and the various things to keep in mind (like non-determinism, AI safety & security, and so on).”
Read the book for free on GitHub (Web)
The Ultimate ChatGPT Guide
Description: “ChatGPT is the most powerful natural language AI ever created. More than 100 resources to help you learn how to use ChatGPT to enhance your life. This Guide includes: 60+ Chapters, 100+ AI Tools and Resources, Save 100+ hours on research”
This is actually a free Gumroad course (“Pay What You Want”)
Speech and Language Processing by Jurafsky and Martin
Blurb: This book on Natural Language Processing (NLP) is a thorough and updated guide, ideal for anyone interested in the field, from students to seasoned practitioners. It delves into the core algorithms and techniques that form the foundation of NLP, as well as exploring various applications and the intricacies of linguistic structure. The text is divided into three key sections.
The first part focuses on fundamental algorithms, introducing the reader to the basics such as text normalization and edit distance, and advances to complex topics like neural networks and language models. This section blends newly written content with updated material from the previous edition.
The second part shifts the focus to practical applications of NLP, covering a wide range of topics from machine translation and chatbots to speech recognition, demonstrating the real-world utility of the techniques discussed.
The final section is dedicated to the annotation of linguistic structures. It offers an in-depth exploration of grammatical and semantic aspects, including parsing, sentence meaning, and discourse coherence, providing a comprehensive understanding of language processing.
Read the PDF on Stanford.edu here (PDF) or the HTML and PowerPoint version here (HTML, pptx)
Foundations of Statistical Natural Language Processing by Manning/Schütze
Description: Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.
Read the PDF here (+Download) and Stanford Online (HTML)
Pattern Recognition and Machine Learning by Christopher M. Bishop
Description: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Read the PDF here (+Download)
Natural Language Processing with Python by Bird, Klein, Loper
Description: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you’ll learn how to write Python programs that work with large collections of unstructured text. You’ll access richly annotated datasets using a comprehensive range of linguistic data structures, and you’ll understand the main algorithms for analyzing the content and structure of written communication.
Read the book here (HTML)
The Illustrated Transformer
Also check out this excellent resource on LLMs that is nicely illustrated:
January 13, 2024 at 01:00AM
Click here for more details...
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