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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

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781788830645

Length 372 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Computer Vision

Authors (2):

Eliot Andres

Benjamin Planche

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Table of Contents (16) Chapters

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

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Migrating from TensorFlow 1 to TensorFlow 2

Since TensorFlow 2 has only been released very recently, most of the projects that are available online are still built for TensorFlow 1. While this first version was already packed with useful features, such as AutoGraph and the Keras API, it is recommended that you migrate to the latest version of TensorFlow so as to avoid any technical debt. Thankfully, TensorFlow 2 comes with an automatic migration tool that is able to convert most projects to its latest version. It requires little effort and outputs functional code. However, migrating to idiomatic TensorFlow 2 code requires some diligence and knowledge of both versions. In this section, we will introduce the migration tool and compare TensorFlow 1 concepts with their TensorFlow 2 counterparts.

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Authors (2)

Benjamin Planche

Dr. Benjamin Planche is a passionate research scientist in computer vision and machine learning. His main research efforts focus on data scarcity problems and industrial vision systems, leading to numerous patents and publications at international conferences. He worked in various research labs around the world (including in France, Japan, Germany, and the USA). Benjamin obtained his Ph.D. summa cum laude from the Faculty of Computer Science and Mathematics at the University of Passau, under the supervision of Prof. Dr. Harald Kosch. He also has a double master's degree from INSA-Lyon (France) and the University of Passau (Germany), with first-class honors and a multinational excellence award. He also likes sharing his knowledge and experience on various platforms or applying them to the creation of aesthetic demos.

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Eliot Andres

Eliot Andres is a freelance deep learning and computer vision engineer. He has more than 3 years' experience in the field, applying his skills to a variety of industries, such as banking, health, social media, and video streaming. Eliot has a double master's degree from cole des Ponts and Tlcom, Paris. His focus is industrialization: delivering value by applying new technologies to business problems. Eliot keeps his knowledge up to date by publishing articles on his blog and by building prototypes using the latest technologies.

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