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The Deep Learning Workshop

You're reading from   The Deep Learning Workshop Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839219856
Length 474 pages
Edition 1st Edition
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Authors (5):
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Nipun Sadvilkar Nipun Sadvilkar
Author Profile Icon Nipun Sadvilkar
Nipun Sadvilkar
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Mohan Kumar Silaparasetty Mohan Kumar Silaparasetty
Author Profile Icon Mohan Kumar Silaparasetty
Mohan Kumar Silaparasetty
Mirza Rahim Baig Mirza Rahim Baig
Author Profile Icon Mirza Rahim Baig
Mirza Rahim Baig
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Toc

Table of Contents (9) Chapters Close

Preface
1. Building Blocks of Deep Learning 2. Neural Networks FREE CHAPTER 3. Image Classification with Convolutional Neural Networks (CNNs) 4. Deep Learning for Text – Embeddings 5. Deep Learning for Sequences 6. LSTMs, GRUs, and Advanced RNNs 7. Generative Adversarial Networks Appendix

Working with Sequences

Let's look at another example to make the importance of sequence modeling clearer. The task is to predict the stock price for a company for the next 30 days. The data provided to you is the stock price for today. You can see this in the following plot, where the y-axis represents the stock price and the x-axis denotes the date. Is this data sufficient?

Figure 5.1: Stock price with just 1 day's data

Surely, one data point, that is, the price on a given day, is not sufficient to predict the price for the next 30 days. We need more information. Particularly, we need information about the past – how the stock price has been moving for the past few days/months/years. So, we ask for, and get, data for three years:

Figure 5.2: Stock price prediction using historical data

This seems much more useful, right? Looking at the past trend and some patterns in the data, we can make predictions on the...

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