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Michael Beyeler – författare
Visar alla böcker från författaren Michael Beyeler. Handla med fri frakt och snabb leverans.
4 produkter
4 produkter
- E-bokEngelska, 2017
533 kr
Läs direkt efter köp
Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.
About This Book
Load, store, edit, and visualize data using OpenCV and PythonGrasp the fundamental concepts of classification, regression, and clusteringUnderstand, perform, and experiment with machine learning techniques using this easy-to-follow guideEvaluate, compare, and choose the right algorithm for any taskWho This Book Is For
This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks.
What You Will Learn
Explore and make effective use of OpenCV''s machine learning moduleLearn deep learning for computer vision with PythonMaster linear regression and regularization techniquesClassify objects such as flower species, handwritten digits, and pedestriansExplore the effective use of support vector machines, boosted decision trees, and random forestsGet acquainted with neural networks and Deep Learning to address real-world problemsDiscover hidden structures in your data using k-means clusteringGet to grips with data pre-processing and feature engineeringIn Detail
Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today''s most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google''s DeepMind.
OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for.
Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today''s hottest topic in the field: Deep Learning.
By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
Style and approach
OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.
- Häftad, Engelska, 2015
590 kr
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- Häftad, Engelska, 2019
651 kr
Skickas inom 5-8 vardagar
A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent image processing systems using OpenCV 4Key FeaturesGain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learnGet up to speed with Intel OpenVINO and its integration with OpenCV 4Implement high-performance machine learning models with helpful tips and best practicesBook DescriptionOpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition.You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system.By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.What you will learnUnderstand the core machine learning concepts for image processingExplore the theory behind machine learning and deep learning algorithm designDiscover effective techniques to train your deep learning modelsEvaluate machine learning models to improve the performance of your modelsIntegrate algorithms such as support vector machines and Bayes classifier in your computer vision applicationsUse OpenVINO with OpenCV 4 to speed up model inferenceWho this book is forThis book is for Computer Vision professionals, machine learning developers, or anyone who wants to learn machine learning algorithms and implement them using OpenCV 4. If you want to build real-world Computer Vision and image processing applications powered by machine learning, then this book is for you. Working knowledge of Python programming is required to get the most out of this book.
