May 1, 2024
Updated May 9, 2025
27 minute read
Neural networks are a cornerstone of modern artificial intelligence, powering many of the "smart" technologies we interact with daily. At a high level, a neural network is a computational model inspired by the way biological nervous systems, such as the human brain, process information. It's a method within AI that essentially teaches computers to learn from data in a manner analogous to human learning. This involves a system of interconnected processing units, often called artificial neurons or nodes, that work together to identify patterns, weigh options, and arrive at conclusions.
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Reading list
We've selected 31 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
Neural Networks.
Authored by three leading researchers in the field, this advanced textbook provides a comprehensive and rigorous treatment of deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is suitable for graduate students and researchers with a strong background in machine learning.
A recent book by a renowned author in the field, this text offers a comprehensive introduction to the central ideas underpinning deep learning. It's intended for both newcomers and those with experience. It focuses on enduring concepts and is structured for self-study or as a textbook, with an emphasis on practical value over abstract theory.
This advanced textbook provides a comprehensive and rigorous treatment of neural network design, covering topics such as supervised learning, unsupervised learning, and deep learning. It is suitable for graduate students and researchers with a strong background in mathematics and statistics.
This advanced textbook provides a comprehensive and rigorous treatment of pattern recognition and neural networks, covering topics such as supervised learning, unsupervised learning, and deep learning. It is suitable for graduate students and researchers with a strong background in mathematics and statistics.
This textbook covers both classical and modern models in deep learning, placing an emphasis on understanding the relationship between traditional machine learning and neural networks. It includes discussions on various network architectures and advanced topics. Written for graduate students, researchers, and practitioners, it serves as a solid reference with an application-centric view. It is suitable as a textbook.
Takes a code-first approach to deep learning using the fastai library, which is built on PyTorch. It focuses on practical applications and achieving state-of-the-art results with minimal code. It's well-suited for coders who want to quickly get into building and deploying deep learning models. It's a great resource for practical implementation.
Provides a comprehensive guide to deep learning using the PyTorch framework. It covers building, training, and tuning neural networks with PyTorch tools. It's a practical book suitable for those who want to learn deep learning specifically with PyTorch and understand its core concepts.
Covers machine learning and deep learning with Python, specifically utilizing PyTorch and Scikit-Learn. It provides a good balance of theory and practical implementation, including neural network concepts. It's suitable for those who want to learn both traditional machine learning and deep learning using these popular libraries.
Written by a pioneer in the field, this practical guide provides a comprehensive overview of machine learning, including neural networks. It is suitable for beginners and experienced practitioners alike, and covers topics such as supervised learning, unsupervised learning, and deep learning.
While not solely focused on neural networks, this book provides a comprehensive foundation in pattern recognition and machine learning, with significant coverage of neural network models. It classic text known for its rigorous approach and is suitable for advanced undergraduate or graduate students. It serves as an excellent reference for the mathematical and statistical basis of neural networks.
Delves into the specifics of Recurrent Neural Networks (RNNs), covering architectures from simple RNNs to more advanced gated units like LSTMs. It's suitable for readers who want to understand the theory and applications of RNNs for sequential data. It provides a focused look at a key type of neural network.
Focuses on deep reinforcement learning, a field that heavily utilizes neural networks. It covers applying modern RL methods with deep neural networks. It's suitable for those interested in the intersection of neural networks and reinforcement learning and provides practical implementations.
This practical guide provides a hands-on introduction to machine learning, including neural networks. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. It is suitable for beginners and experienced practitioners alike.
This practical guide provides a comprehensive overview of deep learning, using Fastai and PyTorch. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is suitable for beginners and experienced practitioners alike.
Provides a good introduction to the fundamentals of deep learning, covering essential concepts related to neural networks. It's suitable for those who want a solid understanding of the core principles before moving on to more advanced topics or specific applications.
Is known for its beginner-friendly approach to deep learning and neural networks, focusing on building networks from scratch using Python and NumPy. It aims to provide intuitive understanding through coding examples. This book is suitable for those new to the field and programming, offering a gentle introduction before tackling more complex texts.
This is another classic text by Bishop, focusing specifically on neural networks for pattern recognition. While older, it provides a strong theoretical foundation in the subject. It's a valuable reference for understanding the earlier developments and mathematical basis of neural network models.
Provides a broad overview of the history and development of neural networks and deep learning, discussing key milestones and applications. It offers a historical perspective on the field and its impact. It's valuable for understanding the context and evolution of neural networks.
This introductory textbook provides a comprehensive overview of deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is suitable for beginners and experienced practitioners alike.
Provides a detailed look at supervised learning in feedforward neural networks. It's a classic reference for this specific architecture, covering theoretical aspects and practical considerations. While it predates the deep learning era, it offers in-depth knowledge on the foundational feedforward networks.
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