May 1, 2024
Updated June 16, 2025
16 minute read
An Introduction to the World of Language Modeling
Language Modeling is a fascinating and rapidly evolving field at the intersection of computer science, linguistics, and artificial intelligence. At its core, language modeling involves creating systems that can understand, generate, and work with human language. Think of it as teaching computers to process and produce text or speech in a way that is meaningful and coherent, much like humans do. These models learn the patterns, grammar, and nuances of language by analyzing vast amounts of text data, enabling them to predict the next word in a sentence, translate languages, answer questions, or even generate creative content.
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Find a path to becoming a Language Modeling. Learn more at:
OpenCourser.com/topic/nunhbx/language
Reading list
We've selected 16 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
Language Modeling.
Dives into contemporary topics by focusing on Transformer models, which are central to modern language modeling, including Large Language Models (LLMs). It provides a practical, hands-on approach using the popular Hugging Face library, making it highly relevant for practitioners and those wanting to deepen their understanding of current techniques.
Offers a deep dive into the inner workings of Large Language Models by guiding the reader through building one from scratch using Python and PyTorch. It's ideal for those who want to understand the fundamental components and training process of LLMs.
This widely referenced and highly recommended book for gaining a broad understanding of NLP, including foundational concepts relevant to language modeling. It is suitable for undergraduate and graduate students and is often used as a textbook in academic institutions. The third edition is being actively developed, incorporating recent advancements.
Known for clear visual explanations, Jay Alammar, along with Maarten Grootendorst, provides a practical guide to working with LLMs. The book focuses on deploying LLMs using open-source tools and offers intuitive diagrams and examples.
Focuses specifically on Transformer models, which are essential for understanding contemporary language modeling. It provides a practical introduction to applying models like BERT and GPT to various NLP tasks.
This concise book provides a hands-on guide to language models, starting from machine learning fundamentals and progressing to modern architectures like Transformers and LLMs. It balances mathematical foundations with practical Python code examples, making complex concepts accessible.
Explores Transformer architectures in detail, covering various models like BERT, T5, and GPT-2. It takes an apply-as-you-learn approach with Python examples, making it practical for understanding and implementing modern language models.
Focuses on building applications with LLMs using the LangChain framework. It's highly practical for those who want to work with existing LLMs and develop applications, providing hands-on experience with contemporary tools and techniques.
Considered a classic in the field, this book provides a strong theoretical foundation in statistical NLP, which is crucial for understanding earlier approaches to language modeling. While some topics are dated, the core concepts remain valuable for a deep understanding. It is more suitable for graduate-level students and researchers.
This textbook provides a technical perspective on NLP, synthesizing classical and contemporary machine learning techniques. It's suitable for advanced undergraduate and graduate students and serves as a good reference for understanding the methods behind language processing.
Provides a practical introduction to using PyTorch for NLP tasks, including building language models. It covers the fundamentals of deep learning for NLP and demonstrates how to implement models using a popular framework.
Focuses on the practical aspects of building NLP systems, offering insights into applying NLP techniques, including those related to language modeling, in real-world scenarios. It's valuable for understanding the NLP pipeline and various use cases.
Offers a practical introduction to NLP with a focus on using the NLTK library in Python. It's excellent for gaining a broad understanding of fundamental NLP tasks and concepts, providing a solid programming foundation that is useful for working with language models.
Offers a quick introduction to using LLMs like ChatGPT. It focuses on practical strategies and best practices, making it suitable for those who want to quickly start working with and understanding the capabilities of modern language models.
Offers a friendly introduction to NLP concepts and techniques using Python. It's ideal for beginners looking to gain a fundamental understanding of the field before diving into more advanced topics in language modeling.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/nunhbx/language
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