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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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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

What makes learning deep?

Actually, the term deep learning had already been coined back in the 80s, when neural networks first began stacking two or three layers of neurons. As opposed to the early, simpler solutions, deep learning regroups deeper neural networks, that is, networks with multiple hidden layers—additional layers set between their input and output layers. Each layer processes its inputs and passes the results to the next layer, all trained to extract increasingly abstract information. For instance, the first layer of a neural network would learn to react to basic features in the images, such as edges, lines, or color gradients; the next layer would learn to use these cues to extract more advanced features; and so on until the last layer, which infers the desired output (such as predicted class or detection results).

However, deep learning only really started being used from 2006, when Geoff Hinton and his colleagues proposed an effective solution...

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