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Hands-On Computer Vision with TensorFlow 2

You're reading from   Hands-On Computer Vision with TensorFlow 2 Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788830645
Length 372 pages
Edition 1st Edition
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Authors (2):
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Eliot Andres Eliot Andres
Author Profile Icon Eliot Andres
Eliot Andres
Benjamin Planche Benjamin Planche
Author Profile Icon Benjamin Planche
Benjamin Planche
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision FREE CHAPTER
2. Computer Vision and Neural Networks 3. TensorFlow Basics and Training a Model 4. Modern Neural Networks 5. Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6. Influential Classification Tools 7. Object Detection Models 8. Enhancing and Segmenting Images 9. Section 3: Advanced Concepts and New Frontiers of Computer Vision
10. Training on Complex and Scarce Datasets 11. Video and Recurrent Neural Networks 12. Optimizing Models and Deploying on Mobile Devices 13. Migrating from TensorFlow 1 to TensorFlow 2 14. Assessments 15. Other Books You May Enjoy

Computer Vision and Neural Networks

In recent years, computer vision has grown into a key domain for innovation, with more and more applications reshaping businesses and lifestyles. We will start this book with a brief presentation of this field and its history so that we can get some background information. We will then introduce artificial neural networks and explain how they have revolutionized computer vision. Since we believe in learning through practice, by the end of this first chapter, we will even have implemented our own network from scratch!

The following topics will be covered in this chapter:

  • Computer vision and why it is a fascinating contemporary domain
  • How we got there—from local hand-crafted descriptors to deep neural networks
  • Neural networks, what they actually are, and how to implement our own for a basic recognition task
...
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