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Practical Machine Learning and Image Processing : For Facial Recognition, Object Detection, and Pattern Recognition Using Python by Himanshu Singh (2019, Trade Paperback)
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Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python. Release Date: 2019-02-27. Sku: I131-AL-071625-216. Condition: New. Qty Available: 1.
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About this product
Product Identifiers
PublisherApress L. P.
ISBN-101484241487
ISBN-139781484241486
eBay Product ID (ePID)15038611739
Product Key Features
Number of PagesXv, 169 Pages
Publication NamePractical Machine Learning and Image Processing : For Facial Recognition, Object Detection, and Pattern Recognition Using Python
LanguageEnglish
SubjectOptical Data Processing, Intelligence (Ai) & Semantics, Image Processing, Programming / Open Source, Programming Languages / Python, Compilers
Publication Year2019
TypeTextbook
AuthorHimanshu Singh
Subject AreaComputers
FormatTrade Paperback
Dimensions
Item Weight16 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Number of Volumes1 vol.
IllustratedYes
Table Of ContentChapter 1: Installation and Environment Setup Chapter 2: Introduction to Python and Image Processing Chapter 3: Advanced Image Processing using OpenCV Chapter 4: Machine Learning Approaches in Image Processing Chapter 5: Real Time Use Cases Chapter 6: Appendix A
SynopsisGain 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. What You Will Learn 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 Who This Book Is For Data scientists and software developers interested in image processing and computer vision., 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 conceptsin 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. What You Will Learn 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 Who This Book Is For Data scientists and software developers interested in image processing and computer vision.
LC Classification NumberQ334-342
