TensorFlow 2.0 Computer Vision Cookbook

TensorFlow 2.0 Computer Vision Cookbook

Implement machine learning solutions to overcome various computer vision challenges

Updated on Mar, 2021

Programming, Data Science, Python

Formats : PDF, EPUB, MOBI (Downlodable)

ISBN : 9781838820688

Get well versed with state-of-the-art techniques to tailor training processes and boost the performance of computer vision models using machine learning and deep learning techniques

Computer vision is a scientific field that enables machines to identify and process digital images and videos. This book focuses on independent recipes to help you perform various computer vision tasks using TensorFlow.

The book begins by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x’s key features, such as the Keras and tf.data.Dataset APIs. You’ll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection. The book also covers autoencoders in domains such as inverse image search indexes and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO).

Moving on, you’ll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you’ll delve into more advanced topics such as Generative Adversarial Networks (GANs), video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks.

By the end of this TensorFlow book, you’ll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x.

This book is for computer vision developers and engineers, as well as deep learning practitioners looking for go-to solutions to various problems that commonly arise in computer vision. You will discover how to employ modern machine learning (ML) techniques and deep learning architectures to perform a plethora of computer vision tasks. Basic knowledge of Python programming and computer vision is required.

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