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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

Running in the browser using TensorFlow.js

With web browsers packing more and more features every year, it was only a matter of time before they could run deep learning models. Running models in the browser has many advantages:

  • The user does not have anything to install.
  • The computing is done on the user's machine (mobile or computer).
  • The model can sometimes make use of the device's GPU.

The library to run in the browser is called TensorFlow.js (refer to the documentation at https://github.com/tensorflow/tfjs). We will implement our face expression classification application using it.

While TensorFlow cannot take advantage of non-NVIDIA GPUs, TensorFlow.js can use GPUs on almost any device. GPU support in the browser was first implemented to display graphical animations through WebGL (a computer graphics API for web applications, based on OpenGL). Since it involves matrix calculus, it was then repurposed to run deep learning operations.
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