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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

Problems that machine learning can solve


Most machine learning problems belong to one of the following three main categories:

  • In supervised learning, each data point is labeled or associated with a category or value of interest (Chapter 3, First Steps in Supervised Learning). An example of a categorical label is assigning an image as either a cat or dog. An example of a value label is the sale price associated with a used car. The goal of supervised learning is to study many labeled examples like these (called training data) in order to make predictions about future data points (called test data). These predictions come in two flavors, such as identifying new photos with the correct animal (called a classification problem) or assigning accurate sale prices to other used cars (called a regression problem). Don't worry if this seems a little over your head for now--we will have the entirety of the book to nail down the details.
  • In unsupervised learning, data points have no labels associated with them (Chapter 8, Discovering Hidden Structures withUnsupervised Learning). Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its structure. This can mean grouping them into clusters or finding different ways of looking at complex data so that they appear simpler.
  • In reinforcement learning, the algorithm gets to choose an action in response to each data point. It is a common approach in robotics, where the set of sensor readings at one point in time is a data point and the algorithm must choose the robot's next action. It's also a natural fit for Internet of Things applications, where the learning algorithm receives a reward signal at a short time into the future, indicating how good the decision was. Based on this, the algorithm modifies its strategy in order to achieve the highest reward.

These three main categories are illustrated in the following figure:

Main machine learning categories

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