Apress | Building Computer Vision Applications Using Artificial Neural Networks: With Step-By-Step Examples In OpenCV And TensorFlow With Python (2020 EN)

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    Author: Shamshad Ansari
    Full Title: Building Computer Vision Applications Using Artificial Neural Networks: With Step-By-Step Examples In OpenCV And TensorFlow With Python
    Publisher: Apress; 1st ed. edition (September 21, 2020)
    Year: 2020
    ISBN-13: 9781484258873 (978-1-4842-5887-3), (978-1-4842-5886-6)
    ISBN-10: 1484258878, 148425886X
    Pages: 451
    Language: English
    Genre: Educational: Machine Learning
    File type: EPUB (True), PDF (True)
    Quality: 10/10
    Price: 35.30 €


    Apply computer vision and machine learning concepts in developing business and industrial applications using a practical, step-by-step approach.

    The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run the code examples. Section 1 covers the basics of image and video processing with code examples of how to manipulate and extract useful information from the images. You will mainly use OpenCV with Python to work with examples in this section.

    Section 2 describes machine learning and neural network concepts as applied to computer vision. You will learn different algorithms of the neural network, such as convolutional neural network (CNN), region-based convolutional neural network (R-CNN), and YOLO. In this section, you will also learn how to train, tune, and manage neural networks for computer vision. Section 3 provides step-by-step examples of developing business and industrial applications, such as facial recognition in video surveillance and surface defect detection in manufacturing.

    The final section is about training neural networks involving a large number of images on cloud infrastructure, such as Amazon AWS, Google Cloud Platform, and Microsoft Azure. It walks you through the process of training distributed neural networks for computer vision on GPU-based cloud infrastructure. By the time you finish reading Building Computer Vision Applications Using Artificial Neural Networks and working through the code examples, you will have developed some real-world use cases of computer vision with deep learning.


    Learn:
    ✓ Employ image processing, manipulation, and feature extraction techniques
    ✓ Work with various deep learning algorithms for computer vision
    ✓ Train, manage, and tune hyperparameters of CNNs and object detection models, such as R-CNN, SSD, and YOLO
    ✓ Build neural network models using Keras and TensorFlow
    ✓ Discover best practices when implementing computer vision applications in business and industry
    ✓ Train distributed models on GPU-based cloud infrastructure

    Features:
    ✓ Contains real examples that you can implement and modify to build useful computer vision systems
    ✓ Gives line-by-line explanations of computer vision working code examples
    ✓ Explains training neural networks involving large numbers of images on cloud infrastructure, such as Amazon AWS, Google Cloud Platform, and Microsoft Azure

    Who This Book Is For:
    Data scientists, analysts, and machine learning and software engineering professionals with Python programming knowledge.

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