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Computer Vision Projects with OpenCV and Python 3

You're reading from   Computer Vision Projects with OpenCV and Python 3 Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789954555
Length 182 pages
Edition 1st Edition
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Author (1):
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Matthew Rever Matthew Rever
Author Profile Icon Matthew Rever
Matthew Rever
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Table of Contents (9) Chapters Close

Preface 1. Setting Up an Anaconda Environment FREE CHAPTER 2. Image Captioning with TensorFlow 3. Reading License Plates with OpenCV 4. Human Pose Estimation with TensorFlow 5. Handwritten Digit Recognition with scikit-learn and TensorFlow 6. Facial Feature Tracking and Classification with dlib 7. Deep Learning Image Classification with TensorFlow 8. Other Books You May Enjoy

Acquiring and processing MNIST digit data

As mentioned, we will be covering handwritten digit recognition with scikit-learn and TensorFlow. Here, we're going to learn how machine learning can be applied to computer vision projects, and we're going to learn a couple of different ways and models, using a couple of different Python modules. Let's get started.

You have probably heard about machine learning. Here, we will be particularly talking about supervised machine learning, where we have a bunch of examples that we want to accomplish. So, rather than explicitly telling the computer what we want, we give an example.

Let's take the case of the handwritten digits 0 through 9, which have labels that are created by humans indicating what those digits are supposed to be. So, rather than hand-coding features and explicitly telling the computer what the algorithm...

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