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Deep Learning with TensorFlow. - Second Edition

You're reading from  Deep Learning with TensorFlow. - Second Edition

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Pages 484 pages
Edition 2nd Edition
Languages
Authors (2):
Giancarlo Zaccone Giancarlo Zaccone
Profile icon Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
View More author details
Toc

Table of Contents (15) Chapters close

Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
1. Getting Started with Deep Learning 2. A First Look at TensorFlow 3. Feed-Forward Neural Networks with TensorFlow 4. Convolutional Neural Networks 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. Heterogeneous and Distributed Computing 8. Advanced TensorFlow Programming 9. Recommendation Systems Using Factorization Machines 10. Reinforcement Learning Index

Data model in TensorFlow


The data model in TensorFlow is represented by tensors. Without using complex mathematical definitions, we can say that a tensor (in TensorFlow) identifies a multidimensional numerical array. We will see more details on tensors in the next subsection.

Tensor

Let's see the formal definition of tensor from Wikipedia (https://en.wikipedia.org/wiki/Tensor):

"Tensors are geometric objects that describe linear relations between geometric vectors, scalars, and other tensors. Elementary examples of such relations include the dot product, the cross product, and linear maps. Geometric vectors, often used in physics and engineering applications, and scalars themselves are also tensors."

This data structure is characterized by three parameters: rank, shape, and type, as shown in the following figure:

Figure 6: Tensors are nothing but geometric objects with a shape, rank, and type, used to hold a multidimensional array

A tensor can thus be thought of as the generalization of a matrix...

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