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

Product type Paperback

Published in Sep 2019

Publisher Packt

ISBN-13 9781789615555

Length 358 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Neural Networks

Author (1):

Paolo Galeone

View More author details

Table of Contents (15) Chapters

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

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Section 2: TensorFlow Fundamentals

This section shows how TensorFlow 2.0 works and the differences compared with version 1.x. This section also covers how to define a complete machine learning pipeline, from data acquisition, passing through the model definition, and how the graph of TensorFlow 1.x is still present in TensorFlow 2.0.

This section comprises the following chapters:

Chapter 3, TensorFlow Graph Architecture
Chapter 4, TensorFlow 2.0 Architecture
Chapter 5, Efficient Data Input Pipelines and Estimator API

The rest of the chapter is locked

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Authors (1)

Galeone

Paolo Galeone is a computer engineer with strong practical experience. After getting his MSc degree, he joined the Computer Vision Laboratory at the University of Bologna, Italy, as a research fellow, where he improved his computer vision and machine learning knowledge working on a broad range of research topics. Currently, he leads the Computer Vision and Machine Learning laboratory at ZURU Tech, Italy. In 2019, Google recognized his expertise by awarding him the title of Google Developer Expert (GDE) in Machine Learning. As a GDE, he shares his passion for machine learning and the TensorFlow framework by blogging, speaking at conferences, contributing to open-source projects, and answering questions on Stack Overflow.

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