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Hands-On Neural Networks with TensorFlow 2.0

You're reading from   Hands-On Neural Networks with TensorFlow 2.0 Understand TensorFlow, from static graph to eager execution, and design neural networks

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Product type Paperback
Published in Sep 2019
Publisher Packt
ISBN-13 9781789615555
Length 358 pages
Edition 1st Edition
Languages
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Author (1):
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Paolo Galeone Paolo Galeone
Author Profile Icon Paolo Galeone
Paolo Galeone
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Neural Network Fundamentals
2. What is Machine Learning? FREE CHAPTER 3. Neural Networks and Deep Learning 4. Section 2: TensorFlow Fundamentals
5. TensorFlow Graph Architecture 6. TensorFlow 2.0 Architecture 7. Efficient Data Input Pipelines and Estimator API 8. Section 3: The Application of Neural Networks
9. Image Classification Using TensorFlow Hub 10. Introduction to Object Detection 11. Semantic Segmentation and Custom Dataset Builder 12. Generative Adversarial Networks 13. Bringing a Model to Production 14. Other Books You May Enjoy

Introduction to Object Detection

Detecting and classifying objects in images is a challenging problem. So far, we have treated the issue of image classification on a simple level; in a real-life scenario, we are unlikely to have pictures containing just one object. In industrial environments, it is possible to set up cameras and mechanical supports to capture images of single objects. However, even in constrained environments, such as an industrial one, it is not always possible to have such a strict setup. Smartphone applications, automated guided vehicles, and, more generally, any real-life application that uses images captured in a non-controlled environment require the simultaneous localization and classification of several objects in the input images. Object detection is the process of localizing an object into an image by predicting the coordinates of a bounding box that...

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