Although Bayesian classifiers are relatively simple to implement, the theory behind them can be quite counter-intuitive at first, especially if you are not too familiar with probability theory yet. However, the beauty of Bayesian classifiers is that they understand the underlying data better than all the classifiers we have encountered so far. For example, standard classifiers, such as the k-nearest neighbor algorithm or decision trees, might be able to tell us the target label of a never-before-seen data point. However, these algorithms have no concept of how likely it is for their predictions to be right or wrong. We call them discriminative models. Bayesian models, on the other hand, have an understanding of the underlying probability distribution that caused the data. We call them generative models because they don't just put labels on...
Machine Learning for OpenCV
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Machine Learning for OpenCV
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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!
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Machine Learning for OpenCV
Table of Contents (13 chapters)
Preface
Free Chapter
A Taste of Machine Learning
Working with Data in OpenCV and Python
First Steps in Supervised Learning
Representing Data and Engineering Features
Using Decision Trees to Make a Medical Diagnosis
Detecting Pedestrians with Support Vector Machines
Implementing a Spam Filter with Bayesian Learning
Discovering Hidden Structures with Unsupervised Learning
Using Deep Learning to Classify Handwritten Digits
Combining Different Algorithms into an Ensemble
Selecting the Right Model with Hyperparameter Tuning
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