Machine Learning for OpenCV

By : Michael Beyeler, Michael Beyeler (USD)

4.4 (13)

Buy this Book

Machine Learning for OpenCV

4.4 (13)

By: Michael Beyeler, Michael Beyeler (USD)

Buy this Book

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!

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

A Taste of Machine Learning

Getting started with machine learning

Problems that machine learning can solve

Getting started with Python

Getting started with OpenCV

Installation

Summary

Working with Data in OpenCV and Python

Understanding the machine learning workflow

Dealing with data using OpenCV and Python

Summary

First Steps in Supervised Learning

Understanding supervised learning

Using classification models to predict class labels

Using regression models to predict continuous outcomes

Classifying iris species using logistic regression

Summary

Representing Data and Engineering Features

Understanding feature engineering

Preprocessing data

Understanding dimensionality reduction

Representing categorical variables

Representing text features

Representing images

Summary

Using Decision Trees to Make a Medical Diagnosis

Understanding decision trees

Using decision trees to diagnose breast cancer

Using decision trees for regression

Summary

Detecting Pedestrians with Support Vector Machines

Understanding linear support vector machines

Dealing with nonlinear decision boundaries

Detecting pedestrians in the wild

Summary

Implementing a Spam Filter with Bayesian Learning

Understanding Bayesian inference

Implementing your first Bayesian classifier

Classifying emails using the naive Bayes classifier

Summary

Discovering Hidden Structures with Unsupervised Learning

Understanding unsupervised learning

Understanding k-means clustering

Understanding expectation-maximization

Compressing color spaces using k-means

Classifying handwritten digits using k-means

Organizing clusters as a hierarchical tree

Summary

Using Deep Learning to Classify Handwritten Digits

Understanding the McCulloch-Pitts neuron

Understanding the perceptron

Implementing your first perceptron

Understanding multilayer perceptrons

Getting acquainted with deep learning

Classifying handwritten digits

Summary

Combining Different Algorithms into an Ensemble

Understanding ensemble methods

Combining decision trees into a random forest

Using random forests for face recognition

Implementing AdaBoost

Combining different models into a voting classifier

Summary

Selecting the Right Model with Hyperparameter Tuning

Evaluating a model

Understanding cross-validation

Estimating robustness using bootstrapping

Assessing the significance of our results

Tuning hyperparameters with grid search

Scoring models using different evaluation metrics

Chaining algorithms together to form a pipeline

Summary

Wrapping Up

Approaching a machine learning problem

Building your own estimator

Where to go from here?

Summary

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Understanding expectation-maximization

K-means clustering is but one concrete application of a more general algorithm known as expectation-maximization. In short, the algorithm works as follows:

Start with some random cluster centers.
Repeat until convergence:

- Expectation step: Assign all data points to their nearest cluster center.
- Maximization step: Update the cluster centers by taking the mean of all the points in the cluster.

Here, the expectation step is so named because it involves updating our expectation of which cluster each point in the dataset belongs to. The maximization step is so named because it involves maximizing a fitness function that defines the location of the cluster centers. In the case of k-means, maximization is performed by taking the arithmetic mean of all the data points in a cluster.

This should become clearer with the following figure: