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portada Building Computer Vision Applications Using Artificial Neural Networks: With Examples in Opencv and Tensorflow With Python
Type
Physical Book
Publisher
Author
Language
English
Pages
548
Format
Paperback
ISBN13
9781484298657
Edition No.
2

Building Computer Vision Applications Using Artificial Neural Networks: With Examples in Opencv and Tensorflow With Python

Ansari Shamshad (Author) · Apress · Paperback

Building Computer Vision Applications Using Artificial Neural Networks: With Examples in Opencv and Tensorflow With Python - Ansari Shamshad

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Synopsis "Building Computer Vision Applications Using Artificial Neural Networks: With Examples in Opencv and Tensorflow With Python "

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 networks What You Will Learn Understand 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 system Who This Book Is For Those 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.

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