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Machine Learning for OpenCV

You're reading from Machine Learning for OpenCV Intelligent image processing with Python

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781783980284

Length 382 pages

Edition 1st Edition

Languages

Python

Tools

OpenCV

Concepts

Machine Learning

Authors (2):

Michael Beyeler

Michael Beyeler (USD)

View More author details

Table of Contents (13) Chapters

Preface

1. A Taste of Machine Learning

2. Working with Data in OpenCV and Python FREE CHAPTER

3. First Steps in Supervised Learning

4. Representing Data and Engineering Features

5. Using Decision Trees to Make a Medical Diagnosis

6. Detecting Pedestrians with Support Vector Machines

7. Implementing a Spam Filter with Bayesian Learning

8. Discovering Hidden Structures with Unsupervised Learning

9. Using Deep Learning to Classify Handwritten Digits

10. Combining Different Algorithms into an Ensemble

11. Selecting the Right Model with Hyperparameter Tuning

12. Wrapping Up

What you need for this book

You will need a computer, Python Anaconda, and enthusiasm. Lots of enthusiasm. Why Python?, you may ask. The answer is simple: it has become the de facto language of data science, thanks to its great number of open source libraries and tools to process and interact with data.

One of these tools is the Python Anaconda distribution, which provides all the scientific computing libraries we could possibly ask for, such as NumPy, SciPy, Matplotlib, Scikit-Learn, and Pandas. In addition, installing OpenCV is essentially a one-liner. No more flipping switches in cc make or compiling from scratch! We will talk about how to install Python Anaconda in Chapter 1, A Taste of Machine Learning.

If you have mostly been using OpenCV in combination with C++, that's fine. But, at least for the purpose of this book, I would strongly suggest that you switch to Python. C++ is fine when your task is to develop high-performance code or real-time applications. But when it comes to picking up a new skill, I believe Python to be a fundamentally better choice of language, because you can do more by typing less. Rather than getting annoyed by the syntactic subtleties of C++, or wasting hours trying to convert data from one format into another, Python will help you concentrate on the topic at hand: to become an expert in machine learning.

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Authors (2)

Michael Beyeler (USD)

Michael Beyeler (USD)

Michael Beyeler is a postdoctoral fellow in neuroengineering and data science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye).His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. He is also an active contributor to several open source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Michael received a PhD in computer science from the University of California, Irvine, and an MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland.

See other products by Michael Beyeler (USD)

Michael Beyeler

Michael Beyeler

Michael Beyeler is a postdoctoral fellow in neuroengineering and data science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye).His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. He is also an active contributor to several open source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Michael received a PhD in computer science from the University of California, Irvine, and an MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland.

See other products by Michael Beyeler

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