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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

Technical requirements

Throughout this book, we will be using Python 3.5 (or higher). As a general-purpose programming language, Python has become the main tool for data scientists thanks to its useful built-in features and renowned libraries.

For this introductory chapter, we will only use two cornerstone libraries—NumPy and Matplotlib. They can be found at and installed from www.numpy.org and matplotlib.org. However, we recommend using Anaconda (www.anaconda.com), a free Python distribution that makes package management and deployment easy.

Complete installation instructions—as well as all the code presented alongside this chapter—can be found in the GitHub repository at github.com/PacktPublishing/Hands-On-Computer-Vision-with-TensorFlow2/tree/master/Chapter01.

We assume that our readers already have some knowledge of Python and a basic understanding of...
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