Best Computer Vision & Pattern Recognition Books

Computer vision is a subfield of artificial intelligence concerned with understanding the content of digital images, such as photographs and videos.

1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Author: by Aurélien Géron
Published at: O'Reilly Media; 2nd edition (October 15, 2019)
ISBN: 978-1492032649

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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.

This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworksScikit-Learn and TensorFlowauthor Aurlien Gron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks.

With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural netsUse Scikit-Learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural nets.


2. Design Patterns: Elements of Reusable Object-Oriented Software

Author: by Erich Gamma
Published at: Addison-Wesley Professional; 1st edition (November 10, 1994)
ISBN: 978-0201633610

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Capturing a wealth of experience about the design of object-oriented software, four top-notch designers present a catalog of simple and succinct solutions to commonly occurring design problems. Previously undocumented, these 23 patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves.

The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently.

Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples.


3. The Hundred-Page Machine Learning Book

Author: by Andriy Burkov
Published at: Andriy Burkov (January 13, 2019)
ISBN: 978-1999579500

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Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics both theory and practice that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field.”Aurlien Gron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: “The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!.

Burkov doesn’t hesitate to go into the math equations: that’s one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field.”Karolis Urbonas, Head of Data Science at Amazon: “A great introduction to machine learning from a world-class practitioner.”Chao Han, VP, Head of R&D at Lucidworks: “I wish such a book existed when I was a statistics graduate student trying to learn about machine learning.”Sujeet Varakhedi, Head of Engineering at eBay: “Andriy’s book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.”Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: “A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.”Vincent Pollet, Head of Research at Nuance: “The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.”Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: “This is a compact how to do data science manual and I predict it will become a go-to resource for academics and practitioners alike.


4. Mathematics for Machine Learning

Author: by Marc Peter Deisenroth
Published at: Cambridge University Press; 1st edition (April 1, 1920)
ISBN: 978-1108455145

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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics.

This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines.

For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding.


5. Advances in Financial Machine Learning

Author: by Marcos Lopez de Prado
Published at: Wiley; 1st edition (February 21, 2018)
ISBN: 978-1119482086

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Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations.

Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives.

The book addresses real life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.


6. Pattern Recognition and Machine Learning (Information Science and Statistics)

Author: by Christopher M. Bishop
Published at: Springer (August 17, 2006)
ISBN: 978-0387310732

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This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning.

No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.


7. Machine Learning Engineering

Author: by Andriy Burkov
Published at: True Positive Inc. (September 5, 2020)
ISBN: 978-1999579579

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From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale.

Andriy Burkov has a Ph.D. In AI and is the leader of a machine learning team at Gartner. This book is based on Andriy’s own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders.

Here’s what Cassie Kozyrkov, Chief Decision Scientist at Google tells about the book in the Foreword:”You’re looking at one of the few true Applied Machine Learning books out there. That’s right, you found one! A real applied needle in the haystack of research-oriented stuff.

Excellent job, dear reader… Unless what you were actually looking for is a book to help you learn the skills to design general-purpose algorithms, in which case I hope the author won’t be too upset with me for telling you to flee now and go pick up pretty much any other machine learning book.


8. Linear Algebra and Learning from Data

Author: by Gilbert Strang
Published at: Wellesley-Cambridge Press; First edition (January 31, 2019)
ISBN: 978-0692196380

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Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets.

This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.


9. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

Author: by Pete Warden
Published at: O'Reilly Media; 1st edition (January 7, 2020)
ISBN: 978-1492052043

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Deep learning networks are getting smaller.Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in sizesmall enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.

Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step.

No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gesturesWork with Arduino and ultra-low-power microcontrollersLearn the essentials of ML and how to train your own modelsTrain models to understand audio, image, and accelerometer dataExplore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyMLDebug applications and provide safeguards for privacy and securityOptimize latency, energy usage, and model and binary size

10. Building Machine Learning Powered Applications: Going from Idea to Product

Author: by Emmanuel Ameisen

Published at: O'Reilly Media; 1st edition (February 11, 2020)
ISBN: 978-1492045113

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Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managersincluding experienced practitioners and novices alikewill learn the tools, best practices, and challenges involved in building a real-world ML application step by step.

Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success.

Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you:Define your product goal and set up a machine learning problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment

11. Information Theory, Inference and Learning Algorithms

Author: by David J. C. MacKay
Published at: Cambridge University Press; 1st edition (September 25, 2003)
ISBN: 978-0521642989

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Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering – communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography.

This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.

The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes – the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay’s groundbreaking book is ideal for self-learning and for undergraduate or graduate courses.

12. Deep Learning for Vision Systems

Author: by Mohamed Elgendy
Published at: Manning Publications; 1st edition (November 10, 2020)
ISBN: 978-1617296192

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How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You’ll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition.

Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL).

Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy’s expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision!

13. Pattern Recognition and Machine Learning (Information Science and Statistics)

Author: by Christopher M. Bishop
Published at: Springer; Softcover reprint of the original 1st ed. 2006 edition (August 23, 2016)
ISBN: 978-1493938438

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This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning.

No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

14. Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play

Author: by David Foster
Published at: O'Reilly Media; 1st edition (July 23, 2019)
ISBN: 978-1492041948

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Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.

Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.

Discover how variational autoencoders can change facial expressions in photosBuild practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generationCreate recurrent generative models for text generation and learn how to improve the models using attentionUnderstand how generative models can help agents to accomplish tasks within a reinforcement learning settingExplore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN