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Machine Learning for OpenCV.
Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book* Load, store, edit, and visualize data using OpenCV and Python* Grasp the fundamental concepts of classification, regression, and clustering* Understand, perform, and experi...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 505 | 0 | |a Cover; Copyright; Credits; Foreword; About the Author; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: 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; Getting the latest code for this book; Getting to grips with Python's Anaconda distribution; Installing OpenCV in a conda environment; Verifying the installation; Getting a glimpse of OpenCV's ML module; Summary; Chapter 2: Working with Data in OpenCV and Python. | |
| 505 | 8 | |a Understanding the machine learning workflowDealing with data using OpenCV and Python; Starting a new IPython or Jupyter session; Dealing with data using Python's NumPy package; Importing NumPy; Understanding NumPy arrays; Accessing single array elements by indexing; Creating multidimensional arrays; Loading external datasets in Python; Visualizing the data using Matplotlib; Importing Matplotlib; Producing a simple plot; Visualizing data from an external dataset; Dealing with data using OpenCV's TrainData container in C++; Summary; Chapter 3: First Steps in Supervised Learning. | |
| 505 | 8 | |a Understanding supervised learningHaving a look at supervised learning in OpenCV; Measuring model performance with scoring functions; Scoring classifiers using accuracy, precision, and recall; Scoring regressors using mean squared error, explained variance, and R squared; Using classification models to predict class labels; Understanding the k-NN algorithm; Implementing k-NN in OpenCV; Generating the training data; Training the classifier; Predicting the label of a new data point; Using regression models to predict continuous outcomes; Understanding linear regression. | |
| 505 | 8 | |a Using linear regression to predict Boston housing pricesLoading the dataset; Training the model; Testing the model; Applying Lasso and ridge regression; Classifying iris species using logistic regression; Understanding logistic regression; Loading the training data; Making it a binary classification problem; Inspecting the data; Splitting the data into training and test sets; Training the classifier; Testing the classifier; Summary; Chapter 4: Representing Data and Engineering Features; Understanding feature engineering; Preprocessing data; Standardizing features; Normalizing features. | |
| 505 | 8 | |a Scaling features to a rangeBinarizing features; Handling the missing data; Understanding dimensionality reduction; Implementing Principal Component Analysis (PCA) in OpenCV; Implementing Independent Component Analysis (ICA); Implementing Non-negative Matrix Factorization (NMF); Representing categorical variables; Representing text features; Representing images; Using color spaces; Encoding images in RGB space; Encoding images in HSV and HLS space; Detecting corners in images; Using the Scale-Invariant Feature Transform (SIFT); Using Speeded Up Robust Features (SURF); Summary. | |
| 520 | 8 | |a Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book* Load, store, edit, and visualize data using OpenCV and Python* Grasp the fundamental concepts of classification, regression, and clustering* Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide* Evaluate, compare, and choose the right algorithm for any taskWho This Book Is ForThis book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn* Explore and make effective use of OpenCV's machine learning module* Learn deep learning for computer vision with Python* Master linear regression and regularization techniques* Classify objects such as flower species, handwritten digits, and pedestrians* Explore the effective use of support vector machines, boosted decision trees, and random forests* Get acquainted with neural networks and Deep Learning to address real-world problems* Discover hidden structures in your data using k-means clustering* Get to grips with data pre-processing and feature engineeringIn DetailMachine 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!Style and approachOpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models. | |
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