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Deep Learning with TensorFlow 2 and Keras

You're reading from   Deep Learning with TensorFlow 2 and Keras Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838823412
Length 646 pages
Edition 2nd Edition
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Authors (3):
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Dr. Amita Kapoor Dr. Amita Kapoor
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Dr. Amita Kapoor
Sujit Pal Sujit Pal
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Sujit Pal
Antonio Gulli Antonio Gulli
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Antonio Gulli
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Table of Contents (19) Chapters Close

Preface 1. Neural Network Foundations with TensorFlow 2.0 2. TensorFlow 1.x and 2.x FREE CHAPTER 3. Regression 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Generative Adversarial Networks 7. Word Embeddings 8. Recurrent Neural Networks 9. Autoencoders 10. Unsupervised Learning 11. Reinforcement Learning 12. TensorFlow and Cloud 13. TensorFlow for Mobile and IoT and TensorFlow.js 14. An introduction to AutoML 15. The Math Behind Deep Learning 16. Tensor Processing Unit 17. Other Books You May Enjoy
18. Index

Textual documents

What do text and images have in common? At first glance: very little. However, if we represent a sentence or a document as a matrix, then this matrix is not much different from an image matrix where each cell is a pixel. So, the next question is: how can we represent a piece of text as a matrix?

Well, it is pretty simple: each row of a matrix is a vector that represents a basic unit for the text. Of course, now we need to define what a basic unit is. A simple choice could be to say that the basic unit is a character. Another choice would be to say that a basic unit is a word, yet another choice is to aggregate similar words together and then denote each aggregation (sometimes called clustering or embedding) with a representative symbol.

Note that regardless of the specific choice adopted for our basic units, we need to have a 1:1 map from basic units into integer IDs so that a text can be seen as a matrix. For instance, if we have a document with 10 lines of...

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