Best Computer Vision & Pattern Recognition Books of 2025

The ChatGPT Millionaire is a comprehensive guide on how to make money online using artificial intelligence. The book provides step-by-step instructions on how to create passive income sources, impress customers with high-quality work, and research and promote engaging content. It also includes access to 150+ powerful "Act as" prompts that'll ensure you can use ChatGPT quickly and easily for whatever you need. The book is an excellent resource for individuals who want to leverage AI to make money online. Overall, this is an excellent book that provides valuable insights into the world of AI and its potential to help people make money online easily and quickly.

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.

Design Patterns: Elements of Reusable Object-Oriented Software is a must-read for object-oriented developers. This book provides a catalog of solutions for commonly occurring design problems, allowing designers to create more flexible, elegant, and reusable designs. The authors systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. The book provides numerous examples where using composition rather than inheritance can improve the reusability and flexibility of code. The code examples in C++ or Smalltalk are helpful. This book is a timeless and elegant solution for common problems in software design.

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 book "Mathematics for Machine Learning" provides a comprehensive coverage of the fundamental mathematical tools required to understand machine learning. The book bridges the gap between mathematical and machine learning texts, making it easier for data science or computer science students, or professionals, to efficiently learn the mathematics. It uses these concepts to derive four central machine learning methods, providing a starting point for those learning the mathematics for the first time. The book includes worked examples, exercises, and programming tutorials on the book's website. Overall, this book is an excellent reference text for both newcomers and machine learning researchers and engineers.

2084: Artificial Intelligence and the Future of Humanity is a thought-provoking book that delves into the impact of artificial intelligence and technology on our lives. John Lennox, a mathematician and philosopher, explores the key developments in technological enhancement, bioengineering, and AI, and provides credible answers to the daunting questions that AI poses. He also explains the key insights that Christianity and Scripture have about the nature of human beings, our future, and what separates us from machines. This book is a must-read for anyone interested in the future of humanity and the role of AI in our lives.

Artificial Intelligence For Dummies is a must-read for anyone who wants to understand the basics of AI. The book covers everything from data use, algorithms, special hardware, and more. It simplifies this complex topic for anyone who wants to understand what operates the devices we can't live without. You'll learn about how AI speeds up data gathering and analysis to help you make informed decisions more quickly. The book is an excellent starting point for anyone who wants to separate the reality of artificial intelligence from the hype.

The "Advances in Financial Machine Learning" book is a must-read for investment professionals who want to succeed in modern finance. The book covers the latest machine learning techniques and how to apply them to the big data of finance. Written by Marcos Lpez de Prado, an expert in the field, the book offers a technically sound roadmap for finance professionals to join the wave of machine learning. What sets this book apart is its empirical approach, focusing on real-world data analysis rather than theoretical methods. Overall, this book is an excellent resource for anyone interested in improving investment performance through machine learning.

This book is a must-read for machine-learning engineers and data scientists who want to explore the cutting-edge algorithms in generative deep learning. With practical examples, David Foster demonstrates how to create impressive models like variational autoencoders, generative adversarial networks, and world models. The book also covers how to make models more efficient and creative. The author's expertise in the field is evident throughout the book. Overall, Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play is an excellent resource for anyone interested in artificial intelligence and deep learning.

This book provides an introduction to pattern recognition from the Bayesian standpoint, presenting approximate inference algorithms that allow fast answers in cases where exact solutions are not possible. It uses graphical models to explain probability distributions, making it unique among other machine learning books. Although a basic understanding of multivariate calculus and linear algebra is required, the book includes a self-contained introduction to basic probability theory. This book is a great reference for researchers and practitioners in machine learning, and would be a good choice for advanced statistics courses or reading groups.