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Machine Learning for OpenCV 4

Machine Learning for OpenCV 4

By : Sharma, Michael Beyeler (USD), Vishwesh Ravi Shrimali , Michael Beyeler
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Machine Learning for OpenCV 4

Machine Learning for OpenCV 4

By: Sharma, Michael Beyeler (USD), Vishwesh Ravi Shrimali , Michael Beyeler

Overview of this book

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.
Table of Contents (18 chapters)
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1
Section 1: Fundamentals of Machine Learning and OpenCV
6
Section 2: Operations with OpenCV
11
Section 3: Advanced Machine Learning with OpenCV

Getting acquainted with deep learning

Back when deep learning didn't have a fancy name yet, it was called artificial neural networks. So you already know a great deal about it!

Eventually, interest in neural networks was rekindled in 1986 when David Rumelhart, Geoffrey Hinton, and Ronald Williams were involved in the (re)discovery and popularization of the aforementioned backpropagation algorithm. However, it was not until recently that computers became powerful enough so they could actually execute the backpropagation algorithm on large-scale networks, leading to a surge in deep learning research.

You can find more information on the history and origin of deep learning in the following scientific article: Wang and Raj (2017), On the Origin of Deep Learning, arXiv:1702.07800.

With the current popularity of deep learning in both industry and academia, we are fortunate enough...

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