The 12 Best Neural Network Books

This article is a must-read for those looking to learn more about the exciting field of computer neural networks. It provides an in-depth look at some of the best books on this topic, ranging from introductory guides to advanced texts. Whether you’re just getting started or already have experience with machine learning and deep learning algorithms, these titles offer something for everyone!

1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
   
   Aurélien Géron
   Published in 2019
   
   Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow is the perfect book for those looking to learn about machine learning. Written by Aurélien Géron, a former Googler and expert in the field of AI, this practical guide teaches readers how to utilize two production-ready Python frameworks -Scikit Learn and Tensor Flow – alongside various techniques from linear regression up to deep neural networks. With exercises included in each chapter to help apply what has been learned, any programmer can get started on their journey into understanding intelligent systems. This book dives into convolutional nets, recurrent nets, reinforcement learning as well as training and scaling powerful neural networks; all explained via real world examples using minimal theory. A must have resource if you want an intuitive view of ML concepts together with coding tutorials.

2. The StatQuest Illustrated Guide To Machine Learning
   
   Josh Starmer
   Published in 2022
   
   The StatQuest Illustrated Guide to Machine Learning by Josh Starmer is an engaging and straightforward book that provides readers with a comprehensive overview of the complex principles behind machine learning. It begins at square one, introducing core concepts in clear language before building on those ideas step-by-step until even more advanced topics such as self-driving cars and facial recognition can be understood. The author’s illustrations are especially helpful for visual learners looking to gain further insight into these subjects. Accompanying this wealth of information is an easy-to-follow format which makes it accessible for any skill level from beginner to expert alike. An extensive appendix offers additional reference material that will prove useful when revisiting or reviewing key topics covered within the main text itself. Overall, anyone interested in learning about this rapidly expanding field should consider reading “The StatQuest Illustrated Guide To Machine Learning”.

3. Deep Learning for Coders with Fastai and PyTorch
   
   Jeremy Howard, Sylvain Gugger
   Published in 2020
   
   Deep Learning for Coders with Fastai and PyTorch, by Jeremy Howard and Sylvain Gugger, is a comprehensive guide that makes deep learning accessible to all. It offers readers the opportunity to learn about the latest techniques in this field without requiring any prior knowledge of mathematics or data science. This book contains detailed instructions on how to train models using fastai and PyTorch on tasks ranging from computer vision to natural language processing. Additionally, it provides insight into ethical considerations when working with machine learning algorithms as well as guidance on turning your projects into web applications. With step-by-step tutorials supported by Jupyter notebooks, anyone can get started quickly on their own deep learning journey using this invaluable resource.

4. The Hundred-Page Machine Learning Book
   
   Andriy Burkov
   Published in 2019
   
   The Hundred-Page Machine Learning Book, written by Andriy Burkov and self-published, provides an excellent overview of the field in just a hundred pages. It covers all major machine learning approaches – including classical linear and logistic regression, support vector machines, deep learning, boosting and random forests – as well as providing mathematical equations for further study. The book is suitable both to newcomers with no prior knowledge of ML or programming experience; experienced practitioners can also gain from its broad view on the topic. Its innovative companion wiki page allows readers to explore topics more deeply if they wish. Reviews are unanimously positive: it has been praised by several leading figures in AI such as Peter Norvig (Research Director at Google) who said it succeeds “well in choosing the topics — both theory and practice — that will be useful to practitioners”. Furthermore Deepak Agarwal (VP of Artificial Intelligence at LinkedIn) called this “a great practical guide”, while Karolis Urbonas (Head of Data Science at Amazon) described it as “a great introduction” from a world class practitioner. Highly recommended for anyone looking to learn about machine learning without needing an enormous amount of time investment!

5. Deep Learning with Python, Second Edition
   
   Francois Chollet
   Published in 2021
   
   Deep Learning with Python, Second Edition is an outstanding book by François Chollet that provides a comprehensive guide to understanding and implementing the groundbreaking advances of deep learning. Packed full of color illustrations, practical examples and clear explanations, this revised edition introduces readers to Keras and TensorFlow – two powerful tools for building neural networks. With no prior experience in mathematics or data science required, it teaches users how to get started with deep-learning applications such as automated language translation, image recognition, timeseries forecasting and text classification. It also covers more advanced topics like generative deep learning best practices for real world application. This authoritative resource offers insights into core principles which will help perfect readers’ neural network skillsets; making Deep Learning with Python an essential read for both novice and experienced machine learning practitioners alike.

6. Ace the Data Science Interview
   
   Nick Singh, Kevin Huo
   Published in 2021
   
   Ace the Data Science Interview is a comprehensive and concise guide to help readers get their dream job in data science. Authored by two ex-Facebook employees, this 301 page book contains 201 real interview questions asked by FAANG companies, tech startups and Wall Street with detailed solutions. It covers topics such as probability, statistics, machine learning, SQL & database design, coding (Python), product analytics and A/B testing. Additionally it includes tips on crafting resumes to break into the field of data science and advice on how best to tell one’s story during behavioral interviews. The authors also explain open ended case study questions that combine critical thinking skills along with statistical modelling techniques through examples from Airbnb, Instagram and Accenture. This invaluable resource has already helped many land their dream jobs in tech or finance industries – making Ace the Data Science Interview an essential read for anyone looking for success in a data related role!

7. You Look Like a Thing and I Love You
   
   Janelle Shane
   Published in 2021
   
   Janelle Shane’s book, You Look Like a Thing and I Love You is an entertaining introduction to the complex world of Artificial Intelligence (AI). It provides insight into how machines learn and their potential for both good and bad applications. With humor and illustrations, this work explores topics such as designing sandwiches with AI or creating Harry Potter fan fiction. The author has a PhD in electrical engineering so readers can be sure that the material presented is accurate but accessible enough for all audiences. Readers will gain understanding on why we trust AI with tasks big and small from unlocking our phones to hospital care. This funny yet informative guide shows us what happens when machines get human things wrong – making it essential reading for anyone curious about this fascinating topic!

8. Machine Learning with PyTorch and Scikit-Learn
   
   Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili, Dmytro Dzhulgakov
   Published in 2022
   
   This Machine Learning with PyTorch and Scikit-Learn book is an authoritative guide for those looking to learn or deepen their understanding of machine learning. It covers all the essential techniques in detail, from basic algorithms such as logistic regression to more recent topics like reinforcement learning and graph neural networks. The clear explanations and visualizations are complemented by real-world examples coded up using popular libraries like PyTorch Lightning, XGBoost, and others. This comprehensive reference also offers best practices on how to evaluate models and tune parameters. As a go-to resource for building practical applications with Pythonic ease, this book should be at the top of your reading list!

9. Deep Learning: A Visual Approach
   
   Andrew Glassner
   Published in 2021
   
   Deep Learning: A Visual Approach is an excellent introduction to the field of deep learning. Written by Andrew Glassner, it provides clear and concise explanations of complex concepts without relying on heavy math or programming skills. Its conversational style and colorful illustrations make it easy to understand key elements such as text generators, image recognition systems, probability theory, and machine learning techniques. This comprehensive guide offers real-world examples along with Python notebooks in a Github repository to help readers write their own programs. Deep Learning: A Visual Approach appeals both to those wanting basic knowledge about this fascinating area as well as experienced AI enthusiasts looking for more detailed information – a must-read for anyone interested in the future of artificial intelligence!

10. Learning Deep Learning
    
    Magnus Ekman
    Published in 2021
    
    Learning Deep Learning by Magnus Ekman is an essential guide to deep learning. It offers readers concise, well-annotated code examples using both TensorFlow with Keras and PyTorch providing comprehensive coverage of the two dominant Python libraries for DL used in industry and academia. This book covers all components needed to succeed including perceptrons, gradient-based learning, sigmoid neurons, back propagation etc., as well as how they are used to build advanced architectures like Mask R-CNN, GPT and BERT. It even touches on ethical issues raised by neural architecture searches (NAS). A great feature of this book is that it does not require prior machine learning or statistics experience making it ideal for developers, data scientists, analysts and others looking to get into the world of deep learning .

11. Machine Learning Engineering
    
    Andriy Burkov
    Published in 2020
    
    Machine Learning Engineering, a book by Andriy Burkov with an impressive list of credentials and experience in AI problem-solving, is the complete guide to reliable machine learning solutions. It offers perspectives on each step from decision-making to product management and data engineering analysis. With its comprehensive coverage of best practices and design patterns for scaling up ML systems, this book stands out among other works that focus only on research topics. In addition, it provides readers with insights into monitoring models during maintenance as well as strategies for anticipating mistakes or dealing with adversaries who try to exploit your system. What makes this book so exceptional is how it embraces the knowledge that mistakes are possible – and how one can prevent them – instead of providing a false sense security about building an “intelligent” AI system which should be avoided at all costs! Machine Learning Engineering is undoubtedly essential reading material for anyone looking to use ML technology in their work setting; making it a must have item!

12. Book of Why
    
    Judea Pearl
    Published in 2020
    
    In The Book of Why, award-winning computer scientist and statistician Judea Pearl investigates how understanding causality can revolutionize science. It provides an in-depth look at correlation versus causation, explaining the difference between these two concepts. He explains why it is important to understand cause and effect if we want to answer difficult questions such as whether a drug cured an illness or why cigarettes were around for years before they showed they caused cancer or heart disease. This book offers readers conceptual tools needed to judge what big data can deliver – something that will be essential for those wanting to explore artificial intelligence further. Through this work, Pearl reveals both the essence of human thought and key insights into AI’s future potential. For anyone looking for answers on causal thinking, this book provides clear explanations with plenty of examples along the way making it easily accessible yet still highly informative.