Computer vision and pattern recognition are two of the most important technologies in modern computing. By utilizing powerful algorithms, they enable machines to recognize objects, detect patterns, and make decisions autonomously. With advances in artificial intelligence research continuing to expand our understanding of these complex systems, there is no better time than now to explore the world of the best computer vision books. This article provides reviews for some of the best titles on this topic so that readers can gain a comprehensive overview and decide which book suits their needs best!
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Published in 2019
Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow is an excellent guide for those looking to gain a deeper understanding of machine learning. Written by experienced consultant and trainer Aurélien Géron, this book provides readers with all the necessary tools to implement their own data-learning programs using two production Python frameworks – Scikit Learn and Tensor Flow. Starting from basic linear regression techniques through more advanced deep neural networks, it contains exercises in each chapter so that readers can practice what they have learned. This comprehensive book covers everything from exploring the machine learning landscape to training models such as support vector machines and random forests, diving into convolutional nets and reinforcement learning; making it perfect for any level learner who has some programming experience.
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The Hundred-Page Machine Learning Book
Published in 2019
This Hundred-Page Machine Learning Book, authored by Andriy Burkov is a great resource for anyone wanting to learn about the subject. It covers all major machine learning approaches from linear and logistic regression up to modern methods such as support vector machines, deep learning and random forests. The book does not assume any high level mathematical or programming knowledge so it can be accessed by almost everyone willing to invest in the time needed. With clear explanations of core concepts balanced between theory and practice and featuring Python coding examples, this book offers an invaluable starting point into the field of Machine Learning. For those wishing to extend their knowledge base even further there is also a companion wiki available with additional information on particular methods mentioned throughout the book. Karolis Urbonas Head of Data Science at Amazon praised it saying “A great introduction to machine learning from a world-class practitioner” making this publication essential reading for academics and practitioners alike.
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Mathematics for Machine Learning
Published in 2020
In Mathematics for Machine Learning, readers are presented with a comprehensive and self-contained textbook that bridges the gap between mathematical fundamentals and machine learning concepts. The text covers topics such as linear algebra, calculus, matrix decompositions, probability theory, vector calculus and optimization to provide an understanding of four core methods: linear regression analysis, principal component analysis (PCA), Gaussian mixture models and support vector machines. With its focus on developing intuition through worked examples and exercises in each chapter – along with programming tutorials available online – this book is ideal for those new to machine learning or established professionals looking to refresh their knowledge.
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Pattern Recognition and Machine Learning
Published in 2006
Pattern Recognition and Machine Learning by Chris Bishop is an authoritative guide to the field of pattern recognition from a Bayesian viewpoint. This comprehensive 700-page work presents approximate inference algorithms for situations where exact answers are not practical, utilizing graphical models for probability distributions in machine learning. Written for advanced undergraduates or beginning graduate students, no prior knowledge of these topics is assumed; however some background with multivariate calculus and basic linear algebra is necessary. The book contains an extensive 400 exercises as well as ample backing material that can be used by readers interested in self-study. It covers important statistical techniques including kernel methods and graphical models while providing geometric illustrations and intuition throughout its content. These features make it an excellent choice as a reference book or to form the basis of several advanced statistics courses.
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2084: Artificial Intelligence and the Future of Humanity
Published in 2020
John C. Lennox’s book 2084: Artificial Intelligence and the Future of Humanity, is an insightful exploration into the implications that arise with ever-advancing technology and artificial intelligence. Using his expertise in mathematics, philosophy and science to explain concepts such as machine learning, bioengineering and transhumanism, Dr. Lennox offers a thought provoking analysis on how these developments could alter our understanding of humankind’s place in the universe. He also provides spiritual insights from Christianity to offer alternatives for those who fear a future where machines outpace humans; offering hope for humanity through evidence-based responses about what makes us unique from machines. This book is an essential read for anyone interested in grasping AI’s current capacity and its potential benefits or dangers – ultimately providing readers with credible answers which will bring them real hope for the future of humanity.
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Machine Learning Engineering
Published in 2020
Andriy Burkov’s Machine Learning Engineering is the essential guide to advancing your AI career. This book draws on the author’s own 15 years of experience in solving problems with artificial intelligence, as well as industry leaders’ published experiences. It provides readers with an invaluable insight into what successful machine learning looks like and how it can be scaled up effectively. The reader will learn important principles for deploying a successful machine learning solution, including when and when not to use one; data engineering and analysis; prototyping ML engineering; statistics; production phase ML engineering; reliability engineering; monitoring strategies and model maintenance. Additionally, this book also covers topics such as how to handle mistakes that may occur along the way, dealing with adversaries who try to exploit your system and managing expectations of both human users and machines alike- all vital components for practical machine learning success that are often neglected by other books. If you want your business problem solved using machine learning then Andriy Burkov’s Machine Learning Engineering is definitely worth picking up!
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Hands-On Machine Learning with Scikit-Learn and TensorFlow
Published in 2017
Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron is an accessible and comprehensive guide to the world of machine learning. From linear regression to deep neural networks, readers are provided a step-by-step breakdown of how these systems work and practical examples in Python so they can apply what they have learned. With two production ready frameworks – scikit learn and TensorFlow – this book has everything you need to get started. It also covers various training models such as support vector machines, decision trees, random forests, ensemble methods etc., followed by guidance on scaling your neural nets for success. The accompanying Jupyter Notebooks provide additional material that make it easier than ever before to understand the code presented here without having too much theoretical knowledge or algorithm details thrown at them. This makes Hands On Machine Learning an ideal choice for both beginners looking to break into the field as well as those already experienced but wanting up-to-date information on this rapidly shifting technology landscape.
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Linear Algebra and Learning from Data
Published in 2019
Gilbert Strang’s Linear Algebra and Learning from Data is the perfect textbook for students looking to gain an understanding of linear algebra, while also delving into deep learning. This comprehensive course contains all the necessary material required to understand data analytics; including topics such as four fundamental subspaces, singular value decompositions, probability and statistics, optimization algorithms and neural nets. The book provides a thorough review of traditional linear algebra principles with up-to-date lists of new techniques used in research today. It emphasizes problem solving through its many practice problems which challenge readers to think critically about their subject matter. In addition there are helpful explanations on how various algorithms work making it suitable for those who may not be experts in mathematics or computer science but still interested in Machine Learning applications. Overall this delightful read offers both rigour and clarity that will have you mastering your studies quickly!
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Multiple View Geometry in Computer Vision
Published in 2004
Multiple View Geometry in Computer Vision, by Richard Hartley and Andrew Zisserman, provides an essential guide to understanding the structure of a real world scene. This is one of the best computer vision books, explaining relevant geometric principles and how they can be used to represent objects algebraically so that they can be computed and applied. It covers major developments in theory and practice of scene reconstruction with detailed unified framework. Written for all levels, it features comprehensive background material as well as algorithms which readers can implement step-by-step. The content is clearly explained; perfect for beginners who want to learn more about 3D vision or experienced professionals looking for updated research information. All in all this text serves as an authoritative source on multiple view geometry making it a must-have when studying computer vision techniques.
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Learning From Data
Published in 2012
Learning From Data by Yaser S. Abu-Mostafa et al is an excellent and comprehensive introduction to the world of Machine Learning, providing readers with a balanced mix of both theoretical knowledge and practical heuristics. This book covers all the fundamentals required for understanding learning from data, presented in a story-like fashion that allows readers to learn at their own pace without feeling rushed or overwhelmed. Featuring insights from professors teaching this subject at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI) and National Taiwan University (NTU), this text also includes free online e-Chapters which are regularly updated with the current trends in ML such as deep learning and support vector machines. For those not familiar with multivariable calculus there’s even a ‘Table Of Notation’ included within the book itself so they can refresh themselves if needed. Overall it’s perfect for anyone looking to gain insight into machine learning – especially beginners who may need more guidance on how to get started!
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Advances in Financial Machine Learning
Published in 2018
In Advances in Financial Machine Learning, Marcos López de Prado provides a comprehensive guide to modern financial analysis that is rich with examples and technical solutions. Drawing on years of expertise as an investment manager, he explains the range of problems posed by machine learning (ML) algorithms when applied to finance and how to use them effectively. He argues convincingly against traditional methods which are often overfit or ineffective in practice and offers cutting-edge techniques for readers wanting to stay ahead. The book covers topics such as structuring big data sets, conducting research using ML algorithms, supercomputing approaches and backtesting discoveries while avoiding false positives. It’s ideal for experienced professionals looking for tools needed to succeed in today’s highly sophisticated finance world who already have some knowledge of statistical data analysis. An invaluable resource full of innovative ideas – this groundbreaking work will provide all manner of investors with valuable insight into modern money management practices
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Code: The Hidden Language of Computer Hardware and Software
Published in 1999
Charles Petzold’s book, ‘Code: The Hidden Language of Computer Hardware and Software’ is a must read for anyone who wants to understand the inner workings of computer technology. It provides an in-depth explanation of how computers process information by examining number systems such as decimal, octal, and binary. This work also includes captivating stories about people from around the world developing communication tools with mechanical and electrical devices throughout history. Additionally, it dissects instruction sets on genre-defining Intel 8080 processors to demonstrate which opcodes are used for different operations. Furthermore, readers get to appreciate the underlying beauty that lies within code through Petzolds’s patient prose and humorous narrative style. Ultimately this book will provide insight into concepts like logic gates, machine language and more!
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The Data Science Design Manual
Published in 2017
The Data Science Design Manual is an engaging and comprehensive source of knowledge for those looking to understand the fundamentals of data science. Written by renowned author Stephen Skiena, this book serves as a text-book/reference guide providing insights into essential skills needed when collecting, analyzing and interpreting data. It does not focus on any particular programming language or tool kit but rather provides high-level discussions about important design principles. War Stories are included in each chapter that provide real world applications of these concepts; homework problems offer exercises for self-study; lecture slides with online video lectures are available at www.data-manuals.com and take home lessons emphasize big picture topics from each section respectively. Additionally there are Kaggle challenges recommended and False Starts which highlight why certain approaches may be unsuccessful thus offering insight into avoiding potential pitfalls while exploring the field further. This book is perfect for undergraduate students studying Introduction to Data Science courses as well as practitioners in statistics, computer science, machine learning and related fields undertaking independent research studies
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