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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

Neural networks

The definition of a neural network, as provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielson, in Neural Network Primer—Part I, is as follows:

"A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs."

In practice, we can think of artificial neural networks as a computational model that is based on how the brain is believed to work. Hence, the mathematical model is inspired by biological neurons.

Biological neurons

The main computational units of the brain are known as neurons; in the human nervous system, approximately 86 billion neurons can be found...

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