Requirements: .ePUB, .PDF reader, 69.6 MB
Overview: Image classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involve feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with Deep Neural Networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural network. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight. The book starts from introducing methods for model-driven feature extraction and classification, including basic Computer Vision techniques for extracting high-level semantics from images. The topic of image classification is presented as a thoroughly curated sequence of steps that gradually increase understanding of the working of a fully trainable classifier. Practical exercises in Python/Keras/Tensorflow have been designed to allow for experimental exploration of these concepts. In each chapter, suitable functions from Python modules are briefly introduced to provide students with the necessary tools to conduct these experiments.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://ouo.io/eGm8sn
https://katfile.com/jwj2ty7snavk/An_Int … n.rar.html
https://rapidgator.net/file/0b52ef6087c … n.rar.html.
