May 1, 2024
Updated July 10, 2025
16 minute read
**Large Language Models (LLMs)** are a type of artificial intelligence (AI) that has been developed to process and generate human-like text. LLMs are trained on vast datasets of text, which allows them to learn the patterns and structures of language. This enables them to perform a variety of language-related tasks, such as text generation, translation, summarization, and question answering.
**Why Learn About Large Language Models?**
There are many reasons why someone might want to learn about LLMs. Some people may be interested in learning about LLMs out of curiosity, while others may be interested in using LLMs to develop their career and professional ambitions.
**How to Learn About Large Language Models**
There are many ways to learn about LLMs. One way is to take an online course. There are many online courses available on LLMs, which can provide learners with a comprehensive overview of the topic. Another way to learn about LLMs is to read books and articles about the topic. There are many resources available online that can help learners get started with learning about LLMs.
**Careers Associated with Large Language Models**
There are many careers that are associated with LLMs. Some of these careers include:
- Machine Learning Engineer
- Data Scientist
- Natural Language Processing Engineer
- Computational Linguist
- AI Researcher
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Reading list
We've selected 30 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
LLM.
Offers a practical and visually intuitive introduction to LLMs, covering their architecture, practical applications like semantic search and text generation, and techniques for fine-tuning. It is suitable for readers with some programming experience and provides hands-on guidance for working with LLMs using open-source tools. Published recently, it addresses contemporary LLM topics.
Authored by Hugging Face engineers, this book practical guide to using transformers, the architecture that underpins most modern LLMs. It focuses on the Hugging Face ecosystem and provides hands-on examples for applying transformers to various NLP tasks. is highly relevant for anyone looking to implement and fine-tune LLMs.
Specifically addresses Retrieval-Augmented Generation (RAG), a key technique used to improve the factual accuracy and relevance of LLM outputs by incorporating external knowledge. It is highly relevant to contemporary LLM application development and provides insights into building more reliable LLM systems.
Guides the reader through the process of building an LLM from the ground up using modern frameworks like PyTorch. It offers a detailed, hands-on approach to understanding the internal workings of LLMs, including training and fine-tuning. This is particularly useful for those who want to gain a deep, practical understanding of LLM development.
Focused on the practical aspects of taking LLMs from concept to production, this handbook is valuable for engineers and practitioners. It covers the workflow, tools, and considerations involved in deploying and managing LLMs in real-world applications. bridges the gap between theoretical understanding and practical implementation.
Provides a detailed exploration of transformer models and their application in NLP, including fine-tuning with models like GPT-3 and BERT. It offers practical guidance and code examples for implementing transformer-based solutions. The second edition includes updated content on recent LLMs.
Delves into advanced techniques and cutting-edge methods in LLMs, suitable for those looking to deepen their understanding beyond the basics. It covers complex applications and explores the latest advancements in the field. This good resource for researchers and professionals working with LLMs.
This definitive and comprehensive textbook on deep learning, the core technology behind modern LLMs. It covers the mathematical and conceptual background of deep learning, various deep learning techniques, and their applications. While challenging, it's an indispensable resource for gaining a deep theoretical understanding of the neural network architectures that power LLMs. It classic in the field and widely referenced by researchers and practitioners.
Focuses on using the LangChain framework to build applications powered by LLMs. It is practical and project-oriented, covering how to integrate LLMs with other components to create more complex systems like chatbots and agents. This is highly relevant for developers looking to build real-world LLM applications.
Covers the engineering aspects of building applications using foundation models, which include LLMs. It discusses the practical challenges and considerations in developing and deploying AI systems based on these large pre-trained models. It relevant resource for those looking to operationalize LLMs.
Considered a foundational text in Natural Language Processing (NLP), this book provides a comprehensive introduction to the field, covering both traditional and modern approaches. While not solely focused on LLMs, it lays essential groundwork in linguistic analysis, statistical methods, and machine learning techniques that are prerequisites for understanding LLMs. It is widely used as a textbook in university courses and serves as a valuable reference.
Delves into the critical ethical and cybersecurity considerations surrounding generative AI and LLMs. It addresses important issues such as bias, misinformation, privacy, and security risks associated with LLM deployment. This is essential reading for anyone involved in the responsible development and application of LLMs.
Provides a hands-on approach to NLP using Python, covering techniques from traditional methods to deep learning. The second edition includes updated content on transformers and LLMs, making it relevant for building practical NLP applications. It's a useful resource for those who want to learn by doing and apply NLP concepts to real-world problems.
While this book does not focus specifically on LLMs, it provides a comprehensive foundation in deep learning techniques for natural language processing, which is essential for understanding the inner workings of LLMs.
Following the concise style of Burkov's previous books, this offers a brief yet comprehensive overview of language models, from foundational concepts to modern LLMs. It covers essential theory and includes code examples, making it a good resource for quickly grasping the core ideas and implementing basic models. Its conciseness makes it a valuable quick reference.
Provides an introduction to generative models, of which LLMs are a prominent type. It covers the underlying concepts and various architectures used for generating new content. While it covers more than just text generation, it offers valuable context for understanding how LLMs fit into the broader landscape of generative AI.
This guide provides a practical and accessible introduction to using prominent LLMs like ChatGPT. It focuses on strategies and best practices for leveraging these models effectively for various tasks. This good starting point for users who want to quickly learn how to work with existing LLMs.
While not exclusively about LLMs, this book covers practical text analysis techniques using Python and machine learning. It provides a good foundation in working with text data, which is essential for anyone working with LLMs. It's a useful reference for data scientists and developers.
While not solely focused on LLMs, this book covers the broader process of designing and deploying machine learning systems for production. The principles and practices discussed are highly relevant to building robust and scalable applications that utilize LLMs. It provides valuable context for the engineering challenges involved.
Is geared towards a non-technical audience, focusing on the business implications, strategy, and responsible deployment of LLMs. It provides a high-level overview of LLMs and their potential impact across industries. This good starting point for those who need to understand the strategic and ethical aspects of LLMs without diving into technical details.
A classic in NLP, this book provides a rigorous foundation in the statistical methods that were prevalent before the deep learning era. While older, the statistical concepts and linguistic background covered are still highly relevant for a deep understanding of language modeling. It's more valuable for historical context and foundational knowledge than for contemporary LLM techniques.
Provides a concise introduction to neural network models as applied to NLP tasks. It covers essential concepts like word embeddings and recurrent neural networks, which were precursors to transformer models. While published before the widespread adoption of transformers, it offers valuable insights into the evolution of neural NLP.
Presents a comprehensive theory of language based on the idea that language product of a large, probabilistic language model. It offers a unique perspective on the nature and acquisition of language.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/5eyl24/ll
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