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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

Chapter 9

  1. When measuring a model's inference speed, should you measure with single or multiple images?

Multiple images should be used to avoid measure bias.

  1. Is a model with float32 weights larger or smaller than one with float16 weights?

Float16 weights use about half the space of float32 weights. On compatible devices, they can also be faster.

  1. On iOS devices, should you use Core ML or TensorFlow Lite? What about Android devices?

On iOS devices, we recommend using Core ML where possible as it is available natively and is tightly integrated with the hardware. On Android devices, TensorFlow Lite should be used as there is no alternative.

  1. What are the benefits and limitations of running a model in the browser?

It does not require any installation on the user side and does not require computing power on the server side, making the application almost infinitely scalable.

  1. What is the most important requirement for embedded devices running deep learning algorithms?

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