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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
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.
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Seitenzahl: 401
Veröffentlichungsjahr: 2021
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TensorFlow 2.0 Computer Vision Cookbook
Implement machine learning solutions to overcome various computer vision challenges
Jesús Martínez
BIRMINGHAM—MUMBAI
TensorFlow 2.0 Computer Vision Cookbook
Copyright © 2021 Packt Publishing
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ISBN 978-1-83882-913-1
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Contributors
About the author
Jesús Martínez is the founder of the computer vision e-learning site DataSmarts. He is a computer vision expert and has worked on a wide range of projects in the field, such as a piece of people-counting software fed with images coming from an RGB camera and a depth sensor, using OpenCV and TensorFlow. He developed a self-driving car in a simulation, using a convolutional neural network created with TensorFlow, that worked solely with visual inputs. Also, he implemented a pipeline that uses several advanced computer vision techniques to track lane lines on the road, as well as providing extra information such as curvature degree.
This book is dedicated to my parents, Armando and Maris, who have always pushed me toward excellence.
About the reviewers
Vincent Kok is a maker and a software platform application engineer in the transportation industry. He graduated from University of Science, Malaysia, with an MSc in embedded system engineering. Vincent actively involves himself with the developer community, as well as attending Maker Faire events held around the world, such as in Shenzhen in 2014 and in Singapore and Tokyo in 2015. Designing electronics hardware kits and giving soldering/Arduino classes for beginners are some of his favorite ways to spend his free time. Currently, his focus is on computer vision technology, software test automation, deep learning, and constantly keeping himself up to date with the latest technology.
Rajeev Ratan is a data scientist with an MSc in artificial intelligence from the University of Edinburgh and a BSc in electrical and computer engineering from the University of the West Indies. He has worked in several London tech start-ups as a data scientist, mostly in computer vision. He was a member of Entrepreneur First, a London-based start-up incubator, where he co-founded an Edtech start-up. Later on, he worked in AI tech start-ups involved in the real estate and gambling sectors. Before venturing into data science, Rajeev worked as a radio frequency engineer for 8 years. His research interests lie in deep learning and computer vision. He has created several online courses that are hosted on Udemy, Packt, and Manning Publications.
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