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:
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Reading list
We've selected 27 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.
Provides a concise overview of generative language models, including their history, different types, and applications. It good choice for readers who want to learn more about generative language modeling without getting too bogged down in the technical details.
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.
Provides a detailed overview of neural language models, including their architecture, training algorithms, and applications. It good choice for readers who want to learn more about the technical details of language modeling.
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.
Focuses specifically on the application of neural networks to NLP problems, including language modeling. It provides a comprehensive overview of neural network methods, from traditional approaches to deep learning, which is essential for understanding the models used in contemporary language modeling.
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.
Covers recent deep learning methods applicable to both NLP and speech. It provides state-of-the-art approaches and includes real-world case studies with code, offering hands-on experience with relevant techniques for language modeling in different modalities.
Provides a comprehensive overview of language modeling, including its history, different types, and applications. It good choice for readers who want to learn more about language modeling in Russian.
Provides an overview of language models for natural language processing. It good choice for readers who want to learn more about how language models are used in NLP.
Provides an overview of text summarization with language models. It good choice for readers who want to learn more about how language models are used to summarize text.
Provides an overview of language modeling and its applications in machine translation. It good choice for readers who want to learn more about how language models are used in practice.
Provides a comprehensive overview of statistical language models, including their theory, algorithms, and applications. It good choice for readers who want to learn more about the mathematical foundations of language modeling.
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.
Focuses on applying NLP concepts to real-world problems using Python. It provides a practical perspective on working with text data and building NLP applications, which can help solidify the understanding of how language models are used in practice.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/nunhbx/language
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