Neural networks are a core concept within artificial intelligence and machine learning. These systems are modeled after the human brain, using layers of interconnected nodes—or artificial neurons—to process and analyze complex data.
They play a critical role in powering intelligent technologies such as image recognition, language modeling, autonomous systems, and predictive analytics. Their ability to learn from large datasets and improve performance over time makes them essential in both research and industrial applications.
Understanding neural network architectures, activation functions, backpropagation, and gradient descent is crucial for anyone entering the field. These concepts lay the foundation for deep learning, reinforcement learning, and generative AI.
Best Neural Networks Books
In this post, we highlight a curated selection of the best neural network books that provide comprehensive insights into theory, practical implementation, and real-world use cases across various industries and domains.
How I Built A Trading Neural Network: A Collection Of 5 Books (Trading Neural Network Series)
- Hardcover Book
- Kam, Kenneth (Author)
- English (Publication Language)
- 426 Pages - 03/25/2025 (Publication Date) - Independently published (Publisher)
Neural Networks for Babies: Teach Babies and Toddlers about Artificial Intelligence and the Brain from the #1 Science Author for Kids (Science Gifts for Little Ones) (Baby University)
- Ferrie, Chris (Author)
- English (Publication Language)
- 24 Pages - 03/01/2019 (Publication Date) - Sourcebooks Explore (Publisher)
Neural Networks and Deep Learning: A Textbook
- Hardcover Book
- Aggarwal, Charu C. (Author)
- English (Publication Language)
- 553 Pages - 06/30/2023 (Publication Date) - Springer (Publisher)
Math for Deep Learning: What You Need to Know to Understand Neural Networks
- Kneusel, Ronald T. (Author)
- English (Publication Language)
- 344 Pages - 12/07/2021 (Publication Date) - No Starch Press (Publisher)
The StatQuest Illustrated Guide to Neural Networks and AI: With hands-on examples in PyTorch!!!
- Starmer, Josh (Author)
- English (Publication Language)
- 363 Pages - 01/06/2025 (Publication Date) - Independently published (Publisher)
Baby University Quantum Science Board Book Set: STEM Board for Toddlers (Baby University Board Book Sets)
- Ferrie, Chris (Author)
- English (Publication Language)
- 08/27/2019 (Publication Date) - Sourcebooks Explore (Publisher)
Make Your Own Neural Network
- Rashid, Tariq (Author)
- English (Publication Language)
- 222 Pages - 03/31/2016 (Publication Date) - CreateSpace Independent Publishing Platform (Publisher)
Build a Large Language Model (From Scratch)
- Raschka, Sebastian (Author)
- English (Publication Language)
- 368 Pages - 10/29/2024 (Publication Date) - Manning (Publisher)
Graph Neural Networks in Action
- Broadwater, Keita (Author)
- English (Publication Language)
- 392 Pages - 04/15/2025 (Publication Date) - Manning (Publisher)
Neural Networks and Deep Learning: A Textbook
- Hardcover Book
- Aggarwal, Charu C. (Author)
- English (Publication Language)
- 520 Pages - 09/13/2018 (Publication Date) - Springer (Publisher)
Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics Series)
- Krohn, Jon (Author)
- English (Publication Language)
- 416 Pages - 09/18/2019 (Publication Date) - Addison-Wesley Professional (Publisher)
Deep Learning (The MIT Press Essential Knowledge series)
- Kelleher, John D. (Author)
- English (Publication Language)
- 296 Pages - 09/10/2019 (Publication Date) - The MIT Press (Publisher)
Programming Neural Networks with Python: Your Practical Guide to Building Smart AI Systems with Machine Learning and Deep Learning (Rheinwerk Computing)
- Joachim Steinwendner (Author)
- English (Publication Language)
- 457 Pages - 05/28/2025 (Publication Date) - Rheinwerk Computing (Publisher)
Graph Neural Networks: Foundations, Frontiers, and Applications
- Hardcover Book
- English (Publication Language)
- 725 Pages - 01/04/2022 (Publication Date) - Springer (Publisher)
Talking Nets: An Oral History of Neural Networks
- English (Publication Language)
- 448 Pages - 02/28/2000 (Publication Date) - The MIT Press (Publisher)
Make Your Own Neural Network: An In-depth Visual Introduction For Beginners
- Make Your Own Neural Network: An In depth Visual Introduction For Beginners
- Independently published
- ABIS BOOK
- Taylor, Michael (Author)
- English (Publication Language)
- 248 Pages - 10/04/2017 (Publication Date) - Independently published (Publisher)
The Math of Neural Networks
- Amazon Kindle Edition
- Taylor, Michael (Author)
- English (Publication Language)
- 187 Pages - 09/07/2017 (Publication Date) - Blue Windmill Media (Publisher)
Neural Network Design (2nd Edition)
- Hagan, Martin T (Author)
- English (Publication Language)
- 800 Pages - 09/01/2014 (Publication Date) - Martin Hagan (Publisher)
Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
- Labonne, Maxime (Author)
- English (Publication Language)
- 354 Pages - 04/14/2023 (Publication Date) - Packt Publishing (Publisher)
Neural Networks: A Systematic Introduction
- Used Book in Good Condition
- Rojas, Raúl (Author)
- English (Publication Language)
- 522 Pages - 07/12/1996 (Publication Date) - Springer (Publisher)
Learning about neural networks is a long-term investment in the future of intelligent automation and algorithmic innovation. Whether your focus is computer vision, natural language processing, or AI-driven decision-making, mastering these fundamentals is essential.
The best neural network books offer deep explanations of neural computations, training dynamics, and architectural optimization techniques. They also help readers gain fluency in neural model development, debugging, and deployment.
By building your knowledge systematically, you position yourself to contribute to advancements in artificial intelligence, edge computing, robotics, and data science. These resources support not just skill development, but also critical thinking about ethical AI and responsible innovation.
Use these learning tools to stay competitive, informed, and confident in navigating the evolving landscape of neural computation and machine intelligence.
