building computer vision applications von ansari shamshad (31 Ergebnisse)

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Paperback or Softback. Zustand: New. Building Computer Vision Applications Using Artificial Neural Networks: With Examples in Opencv and Tensorflow with Python 2.08. Book.

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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

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Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 548 | Sprache: Englisch | Produktart: Bücher.

Zustand: Sehr gut. Zustand: Sehr gut - Gepflegter, sauberer Zustand. 2. Auflage. | Seiten: 548 | Sprache: Englisch | Produktart: Bücher.

Paperback. Zustand: Brand New. 2nd edition. 548 pages. 10.01x7.01x1.11 inches. In Stock.

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Paperback. Zustand: Brand New. 451 pages. 9.50x6.75x1.00 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.What You Will 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 infrastructureWho This Book Is ForData scientists, analysts, and machine learning and software engineering professionals with Python programming knowledge.

Zustand: New. 2023. 2nd Edition. paperback. . . . . . Books ship from the US and Ireland.

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Paperback. Zustand: new. Paperback. Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first editions publication. All code used in the book has also been fully updated.This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and youll gain a thorough understanding of them. The books source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python. Upon completing this book, youll have the knowledge and skills to build your own computer vision applications using neural networks What You Will LearnUnderstand image processing, manipulation techniques, and feature extractionmethodsWork with convolutional neural networks (CNN), single-shot detector (SSD), and YOLOUtilize large scale model development and cloud infrastructure deploymentGain an overview of FaceNet neural network architecture and develop a facial recognition systemWho This Book Is ForThose who possess a solid understanding of Python programming and wish to gain an understanding of computer vision and machine learning. It will prove beneficial to data scientists, deep learning experts, and students. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: Brand New. 451 pages. 9.50x6.75x1.00 inches. In Stock.

Zustand: New. Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition s publication. All code used in the book has also been fully updated.This second edition features n.

paperback. Zustand: New. New. book.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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.What You Will 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 infrastructureWho This Book Is ForData scientists, analysts, and machine learning and software engineering professionals with Python programming knowledge. 451 pp. Englisch.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition's publication. All code used in the book has also been fully updated.This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you'll gain a thorough understanding of them. The book's source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python. Upon completing this book, you'll have the knowledge and skills to build your own computer vision applications using neural networksWhat You Will LearnUnderstand image processing, manipulation techniques, and feature extraction methodsWork with convolutional neural networks (CNN), single-shot detector (SSD), and YOLOUtilize large scale model development and cloud infrastructure deploymentGain an overview of FaceNet neural network architecture and develop a facial recognition systemWho This Book Is ForThose who possess a solid understanding of Python programming and wish to gain an understanding of computer vision and machine learning. It will prove beneficial to data scientists, deep learning experts, and students. 548 pp. Englisch.

Paperback / softback. Zustand: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 1031.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition's publication. All code used in the book has also been fully updated.This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you'll gain a thorough understanding of them. The book's source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python. Upon completing this book, you'll have the knowledge and skills to build your own computer vision applications using neural networksWhat You Will LearnUnderstand image processing, manipulation techniques, and feature extractionmethodsWork with convolutional neural networks (CNN), single-shot detector (SSD), and YOLOUtilize large scale model development and cloud infrastructure deploymentGain an overview of FaceNet neural network architecture and develop a facial recognition systemWho This Book Is ForThose who possess a solid understanding of Python programming and wish to gain an understanding of computer vision and machine learning. It will prove beneficial to data scientists, deep learning experts, and students.