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Machine Learning for OpenCV 4
Machine Learning for OpenCV 4

Machine Learning for OpenCV 4: Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn , Second Edition

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Profile Icon Michael Beyeler (USD) Profile Icon Sharma Profile Icon Vishwesh Ravi Shrimali Profile Icon Michael Beyeler
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Mex$561.99 Mex$803.99
eBook Sep 2019 420 pages 2nd Edition
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Mex$561.99 Mex$803.99
Paperback
Mex$1004.99
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Arrow left icon
Profile Icon Michael Beyeler (USD) Profile Icon Sharma Profile Icon Vishwesh Ravi Shrimali Profile Icon Michael Beyeler
Arrow right icon
Mex$561.99 Mex$803.99
eBook Sep 2019 420 pages 2nd Edition
eBook
Mex$561.99 Mex$803.99
Paperback
Mex$1004.99
Subscription
Free Trial
eBook
Mex$561.99 Mex$803.99
Paperback
Mex$1004.99
Subscription
Free Trial

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Machine Learning for OpenCV 4

Section 1: Fundamentals of Machine Learning and OpenCV

In the very first section of this book, we will go over the basics of machine learning and OpenCV, starting with installing the required libraries, and then moving on to basic OpenCV functions, the basics of supervised learning and their applications, and finally, feature detection and recognition using OpenCV.

This section includes the following chapters:

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Key benefits

  • Gain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learn
  • Get up to speed with Intel OpenVINO and its integration with OpenCV 4
  • Implement high-performance machine learning models with helpful tips and best practices

Description

OpenCV 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.

Who is this book for?

This 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.

What you will learn

  • Understand the core machine learning concepts for image processing
  • Explore the theory behind machine learning and deep learning algorithm design
  • Discover effective techniques to train your deep learning models
  • Evaluate machine learning models to improve the performance of your models
  • Integrate algorithms such as support vector machines and Bayes classifier in your computer vision applications
  • Use OpenVINO with OpenCV 4 to speed up model inference

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 06, 2019
Length: 420 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789537192
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Product Details

Publication date : Sep 06, 2019
Length: 420 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789537192
Category :
Languages :
Tools :

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Table of Contents

17 Chapters
Section 1: Fundamentals of Machine Learning and OpenCV Chevron down icon Chevron up icon
A Taste of Machine Learning Chevron down icon Chevron up icon
Working with Data in OpenCV Chevron down icon Chevron up icon
First Steps in Supervised Learning Chevron down icon Chevron up icon
Representing Data and Engineering Features Chevron down icon Chevron up icon
Section 2: Operations with OpenCV Chevron down icon Chevron up icon
Using Decision Trees to Make a Medical Diagnosis Chevron down icon Chevron up icon
Detecting Pedestrians with Support Vector Machines Chevron down icon Chevron up icon
Implementing a Spam Filter with Bayesian Learning Chevron down icon Chevron up icon
Discovering Hidden Structures with Unsupervised Learning Chevron down icon Chevron up icon
Section 3: Advanced Machine Learning with OpenCV Chevron down icon Chevron up icon
Using Deep Learning to Classify Handwritten Digits Chevron down icon Chevron up icon
Ensemble Methods for Classification Chevron down icon Chevron up icon
Selecting the Right Model with Hyperparameter Tuning Chevron down icon Chevron up icon
Using OpenVINO with OpenCV Chevron down icon Chevron up icon
Conclusion Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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