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Machine Learning for OpenCV

Machine Learning for OpenCV

By : Michael Beyeler, Michael Beyeler (USD)
4.4 (13)
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Machine Learning for OpenCV

Machine Learning for OpenCV

4.4 (13)
By: Michael Beyeler, Michael Beyeler (USD)

Overview of this book

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!
Table of Contents (21 chapters)
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Title Page
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

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! This was a respected field in itself, but after the days of Rosenblatt's perceptron, many researchers and machine learning practitioners slowly began to lose interest in the field since no one had a good solution for training a neural network with multiple layers.

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.

Note

You can find more information on the history and origin of deep learning in the following scientific article: Wang & Raj (2017...

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