Best Computer Neural Networks Books of 2025

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a must-read for anyone interested in machine learning. This book provides a comprehensive overview of the concepts and techniques required to build intelligent systems. The author, Aurelien Geron, uses Python frameworks such as scikit-learn, Keras, and TensorFlow to explain everything from simple linear regression to deep neural networks. The book includes numerous code examples and exercises that help readers apply what they've learned. The updated third edition covers new topics such as generative adversarial networks and deep reinforcement learning. Overall, this book is an excellent resource for anyone looking to learn machine learning from scratch or deepen their knowledge in the field.

Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street is a must-read guide for anyone who wants to land their dream job in data science, data analysis, or machine learning. Authored by two ex-Facebook employees, this 301-page book offers comprehensive coverage of the most frequently tested topics in data interviews. It provides detailed step-by-step solutions to 201 real data science interview questions asked by top companies, including Facebook, Google, Amazon, Netflix, Two Sigma, and Citadel. Additionally, the book offers valuable career advice on crafting your resume, creating portfolio projects, networking, and more. Overall, Ace the Data Science Interview is an invaluable resource for anyone looking to break into the data science industry.

All the math we need to get into AI. Math and AI made easy...Many industries are eager to integrate AI and data-driven technologies into their systems and operations. But to build truly successful AI systems, you need a firm grasp of the underlying mathematics. This comprehensive guide bridges the gap in presentation between the potential and applications of AI and its relevant mathematical foundations. In an immersive and conversational style, the book surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-of-the-art models, rather than on dense academic theory. You'll explore topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more within an exclusive AI context geared toward computer vision, natural language processing, generative models, reinforcement learning, operations research, and automated systems. With a broad audience in mind, including engineers, data scientists, mathematicians, scientists, and people early in their careers, the book helps build a solid foundation for success in the AI and math fields. You'll be able to: Comfortably speak the languages of AI, machine learning, data science, and mathematicsUnify machine learning models and natural language models under one mathematical structureHandle graph and network data with easeExplore real data, visualize space transformations, reduce dimensions, and process imagesDecide on which models to use for different data-driven projectsExplore the various implications and limitations of AI

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesLearn applied machine learning with a solid foundation in theoryClear, intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practicesBook DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.Why PyTorch?PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learnExplore frameworks, models, and techniques for machines to 'learn' from dataUse scikit-learn for machine learning and PyTorch for deep learningTrain machine learning classifiers on images, text, and moreBuild and train neural networks, transformers, and boosting algorithmsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho this book is forIf you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.Table of ContentsGiving Computers the Ability to Learn from DataTraining Simple Machine Learning Algorithms for ClassificationA Tour of Machine Learning Classifiers Using Scikit-LearnBuilding Good Training Datasets – Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data – Clustering AnalysisImplementing a Multilayer Artificial Neural Network from Scratch(N.B. Please use the Look Inside option to see further chapters)

"Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD" is a must-read for programmers who want to achieve impressive results in deep learning with minimal code and math background. Jeremy Howard and Sylvain Gugger, the creators of fastai, provide a hands-on guide to train a model on a wide range of tasks using fastai and PyTorch. The book covers the latest deep learning techniques and provides a complete understanding of the algorithms behind the scenes. Overall, it's an excellent resource that simplifies complex concepts and makes deep learning accessible to everyone.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a practical guide to building intelligent systems using machine learning. The author, Aurélien Géron, provides a comprehensive overview of the field, from simple linear regression to deep neural networks, using Python frameworks Scikit-Learn and TensorFlow. With exercises in each chapter, readers can apply what they've learned and gain an intuitive understanding of the concepts and tools. Overall, this book is an excellent resource for programmers with some experience who want to delve into machine learning and build intelligent systems.

The second edition of "Deep Learning with Python" is an extensively revised book that introduces the field of deep learning using Python and the powerful Keras library. With practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks, this book is suitable for both novice and experienced machine learning practitioners. The book is printed in full color and offers crisp illustrations and clear examples throughout. Overall, this book is an excellent resource for those looking to unlock the groundbreaking advances of deep learning and is a must-read for every software developer seeking to expand their knowledge in this field.

The CW Way of Life is a self-contained Morse code learning system that combines traditional techniques with modern training principles. The book not only teaches Morse code but also provides a unique insight into how the brain processes sensations into thoughts. The book is divided into seven parts that build on each other, making it suitable for beginners and advanced learners alike. The author also delves into the psychology and physics of Morse code. The CW Way of Life is a must-read for anyone interested in learning Morse code or gaining a deeper understanding of how the brain works.

The Book of Why by Judea Pearl, a Turing Award-winning computer scientist and statistician, is a groundbreaking work that explores the study of cause and effect on a scientific basis. Pearl's work not only explains how we can determine whether something causes another, but also enables us to explore the worlds that could have been. The book provides valuable lessons on the history of ideas and the conceptual tools needed to judge what big data can and cannot deliver. This book is a must-read for anyone interested in understanding causality, human thought, and artificial intelligence.

The StatQuest Illustrated Guide to Machine Learning is a fantastic resource for anyone interested in machine learning. The author breaks down complex algorithms into easily digestible pieces, with clear illustrations that help readers understand the concepts beyond the equations. The book starts with the basics and gradually builds up to advanced topics such as self-driving cars and facial recognition. The author doesn't dumb down the material, but instead empowers readers to understand it deeply. This book is a must-have for anyone interested in Artificial Intelligence and Semantics.