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The TensorFlow Workshop

You're reading from   The TensorFlow Workshop A hands-on guide to building deep learning models from scratch using real-world datasets

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800205253
Length 600 pages
Edition 1st Edition
Languages
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Authors (4):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Abhranshu Bagchi Abhranshu Bagchi
Author Profile Icon Abhranshu Bagchi
Abhranshu Bagchi
Anthony Maddalone Anthony Maddalone
Author Profile Icon Anthony Maddalone
Anthony Maddalone
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
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Toc

Table of Contents (13) Chapters Close

Preface
1. Introduction to Machine Learning with TensorFlow 2. Loading and Processing Data FREE CHAPTER 3. TensorFlow Development 4. Regression and Classification Models 5. Classification Models 6. Regularization and Hyperparameter Tuning 7. Convolutional Neural Networks 8. Pre-Trained Networks 9. Recurrent Neural Networks 10. Custom TensorFlow Components 11. Generative Models Appendix

Sequential Data

Sequential data is information that happens in a sequence and is related to past and future data. An example of sequential data is time series data; as you perceive it, time only travels in one direction.

Suppose you have a ball (as in Figure 9.2), and you want to predict where this ball will travel next. If you have no prior information about the direction from which the ball was thrown, you will simply have to guess. However, if in addition to the ball's current location, you also had information about its previous location, the problem would be much simpler. To be able to predict the ball's next location, you need the previous location information in a sequential (or ordered) form to make a prediction about future events.

Figure 9.2: Direction of the ball

RNNs function in a way that allows the sequence of the information to retain value with the help of internal memory.

You'll take a look at some examples of sequential...

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