May 13, 2024
Updated July 20, 2025
13 minute read
Transformer Networks are a type of neural network that has revolutionized the field of natural language processing (NLP) and has found applications in a wide range of other domains, including computer vision and machine translation. Transformers are based on the concept of attention, which allows them to focus on specific parts of the input data and learn relationships between different parts of the sequence. This makes them particularly well-suited for tasks involving sequential data, such as text and audio.
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Reading list
We've selected 25 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
Transformer Networks.
Provides a practical, hands-on introduction to transformers using the Hugging Face library, which is widely used in the field. It covers building, training, and fine-tuning transformer models for various NLP tasks. It is an excellent resource for those looking to apply transformers directly and serves as a valuable reference for practitioners.
Highly relevant to contemporary topics, this book explores generative AI techniques, focusing on transformers and diffusion models. It teaches readers how to build and customize models for generating text, images, and more. is excellent for those interested in the cutting edge of generative models and their practical applications.
Considered one of the first comprehensive books on transformers, this text offers detailed explanations of various transformer architectures and techniques. It covers applications beyond NLP, including speech, time series, and computer vision. is particularly useful for postgraduate students and researchers seeking a deep theoretical understanding and a broad overview of the field.
Given the close relationship between transformers and Large Language Models (LLMs), this book is highly relevant for understanding a major application area of transformers. It provides practical guidance on working with LLMs, which are predominantly built upon transformer architectures. It's suitable for those wanting to apply transformers in the context of large-scale language tasks.
Provides a comprehensive overview of the Transformer architecture and its applications in a variety of NLP tasks, including machine translation, text summarization, and question answering.
Focuses on applying transformer models to a wide range of NLP problems using Python, PyTorch, and TensorFlow. It introduces key transformer architectures like BERT and GPT and covers natural language understanding and generation. It practical guide for those wanting to implement transformer-based solutions.
Serves as a comprehensive guide to understanding Large Language Models, with a focus on the underlying transformer architecture. It aims to make LLMs more accessible by explaining their inner workings from mathematical foundations to implementation. It's a good resource for gaining clarity on how transformers power modern LLMs.
Aims to help readers build state-of-the-art models using transformers with advanced NLP techniques. It delves into training language models and fine-tuning pre-trained models for various tasks. This book is for those who want to gain a deeper technical understanding and mastery of transformer implementation.
Focuses on the principles and implementation of generative models. The second edition includes transformers as a key architecture for generating various types of data. It's valuable for understanding the generative capabilities of transformers and is relevant for those interested in creating new content with AI.
This comprehensive and open-source book covers the fundamentals of deep learning, including a section on attention mechanisms and transformers. It provides a solid theoretical foundation and practical implementation details with code examples in multiple frameworks. It's suitable for a broad audience, from undergraduates to professionals, and valuable reference.
Following the style of 'The Hundred-Page Machine Learning Book', this book provides a concise overview specifically of language models, which are heavily reliant on transformers. It offers a quick and accessible introduction to the key concepts and techniques in this area, suitable for those wanting a focused, high-level understanding of LLMs and transformers.
A very popular and practical guide covering a wide range of machine learning and deep learning algorithms using key libraries. The later editions include coverage of transformers and their implementation in TensorFlow and Keras. It serves as an excellent practical reference and provides a broad understanding of ML/DL techniques, including those used with transformers.
Provides a comprehensive overview of Transformer networks for speech recognition. It covers a wide range of topics, including the architecture of Transformer networks, training methods, and applications to various speech recognition tasks.
A widely regarded classic in Natural Language Processing, this book provides comprehensive coverage of foundational NLP concepts and techniques. While earlier editions predate transformers, newer editions and the ongoing third edition draft incorporate them. It's essential for gaining a broad understanding of the field leading to transformers and serves as a key reference and textbook.
Provides a comprehensive overview of Transformer networks for natural language processing (NLP). It covers a wide range of topics, including the architecture of Transformer networks, training methods, and applications to various NLP tasks.
Covers the theory and practice of neural networks and deep learning, including transformers, using TensorFlow. It explains how to build advanced architectures for computer vision and NLP, such as GPT and BERT. It's a good resource for learning deep learning fundamentals with practical examples relevant to transformers.
Offers a practical, code-first approach to deep learning using the fastai library and PyTorch. It covers various deep learning architectures and applications, including those relevant to NLP and sequence models. While not exclusively focused on transformers, it provides a strong practical foundation for implementing deep learning models that can be applied to transformer networks.
This foundational textbook provides a rigorous introduction to deep learning concepts, architectures, and training methods. While it may not have extensive coverage of transformers due to its publication date, the fundamental knowledge it imparts on neural networks, backpropagation, and optimization is crucial for understanding how transformers work. It classic and a must-read for those seeking a deep theoretical basis in deep learning.
Foundational text in statistical NLP, covering essential mathematical and statistical methods. While published before the advent of transformers, the principles of language modeling, probability, and classification discussed are fundamental to understanding the statistical underpinnings of modern NLP models, including transformers. It's a classic for theoretical depth.
Focuses on building neural networks from the ground up using NumPy. It provides a deep understanding of the core mechanics of neural networks, such as forward and backward propagation. This foundational knowledge is highly beneficial for truly grasping the internal workings of complex architectures like transformers. It's valuable for solidifying basic deep learning concepts. (Replaces Michael Nielsen's online book for ISBN requirement).
Provides a comprehensive overview of deep learning for natural language processing (NLP). It covers a wide range of topics, including the different types of deep learning models, their applications to various NLP tasks, and their theoretical foundations.
A classic introductory book to NLP using the NLTK library. While it predates the widespread use of transformers, it provides fundamental knowledge of linguistic concepts, text processing, and basic NLP tasks. This serves as a valuable prerequisite for understanding the problems that transformers are designed to solve.
Covers practical text analysis techniques using Python. While published before transformers became dominant, it provides a solid foundation in processing and analyzing text data, which necessary prerequisite for working with transformer models in NLP. It's a good resource for understanding the data pipeline.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/dycu91/transformer
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