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Key benefits

Develop, train, and use deep learning algorithms for computer vision tasks using TensorFlow 2.x

Discover practical recipes to overcome various challenges faced while building computer vision models

Enable machines to gain a human level understanding to recognize and analyze digital images and videos

Description

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.

Who is this book for?

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.

What you will learn

Understand how to detect objects using state-of-the-art models such as YOLOv3

Use AutoML to predict gender and age from images

Segment images using different approaches such as FCNs and generative models

Learn how to improve your network's performance using rank-N accuracy, label smoothing, and test time augmentation

Enable machines to recognize people's emotions in videos and real-time streams

Access and reuse advanced TensorFlow Hub models to perform image classification and object detection

Generate captions for images using CNNs and RNNs

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

₱2245.99

Hands-On Image Generation with TensorFlow

₱2806.99

₱2245.99

Total ₱ 7,298.97

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Amazon verified reviews

Vipin Singh Jun 01, 2021

Excellent book for data scientist enthusiam to build carrier in Computer Vision using tensorflow 2. This book provides a comprehensive coverage of everything associated with computer vision, right from the very basics to the latest and greatest models and techniques. The book builds onto concepts in a very gradual manner for the beginners while the hands-on examples keep things interesting for all types of readers. The book provides code examples on a variety of topics starting from image classification, CNNs, RNNs, transfer learning, GAN's, Image segmentation and object detection. The examples are well choosen and embibes the core concepts. The best part with the book is it covers topic like AutoML and method to boost performance.

Amazon Verified review

Aishwary Mar 09, 2021

By far, this is one of the best books to understand how to apply deep learning in the field of computer vision. The concepts have been clearly explained. It covers almost everything from image classification, image segmentation, object detection, etc. It introduces both the methods of loading images for model training. One via the Keras API and the other by tf.data (which is the most important module to develop flexible and efficient pipelines). It teaches you how to augment data to improvise a model performance. Then, it goes into the concepts of Auto-encoder, AutoML. This book even has a dedicated chapter about using TensorFlow for video processing. The best part about this book that I liked the most was the references to the relevant materials that the author has provided after each concept explained.

Ashwini Jul 15, 2021

This is an awesome book for those working in Computer Vision. The books starts with the Tensorflow 2.0 introduction and how Keras can be used in Computer Vision. The following chapters dive deeper into Machine Learning models like ResNet and image classification. A great book for those who are starting their career in Computer Vision as well as for those who are already working in it.

LeoQuiroa May 06, 2021

The book covers a variety of topics from basics to experts which makes it attractive for many people interested to work with images and videos. For example, it covers a broad range of algorithms like the classic ones such as Classification but also a lot of new stuff like Transfer Learning, Autoencoders, GAN, and so on. Lastly, the book has a good practice to include a hands-on experience coding for every concept, so you will learn by doing.

Saquib Mohiuddin Apr 18, 2021

one of the best books to understand and applying DL CV concepts in the field of computer vision. The content is simple to understand and concise. covers all the relevant concepts from segmentation to classification.

TensorFlow 2.0 Computer Vision Cookbook: Implement machine learning solutions to overcome various computer vision challenges

What do you get with eBook?