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Practical machine learning and image...

Singh, Himanshu.

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Practical machine learning and image processing = for facial recognition, object detection, and pattern recognition using Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Practical machine learning and image processing/ by Himanshu Singh.
Reminder of title:	for facial recognition, object detection, and pattern recognition using Python /
Author:	Singh, Himanshu.
Published:	Berkeley, CA :Apress : : 2019.,
Description:	xv, 169 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-4149-3
ISBN:	9781484241493

Practical machine learning and image processing = for facial recognition, object detection, and pattern recognition using Python /
Singh, Himanshu.

Practical machine learning and image processingfor facial recognition, object detection, and pattern recognition using Python /[electronic resource] :by Himanshu Singh. - Berkeley, CA :Apress :2019. - xv, 169 p. :ill., digital ;24 cm.

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You'll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You'll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you'll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. You will: Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects.

ISBN: 9781484241493

Standard No.: 10.1007/978-1-4842-4149-3doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Practical machine learning and image processing = for facial recognition, object detection, and pattern recognition using Python /
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300 $a xv, 169 p. : $b ill., digital ; $c 24 cm.
520 $a Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You'll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You'll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you'll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. You will: Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects.
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