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TensorFlow 1.x Deep Learning Cookbook

You're reading from   TensorFlow 1.x Deep Learning Cookbook Over 90 unique recipes to solve artificial-intelligence driven problems with Python

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788293594
Length 536 pages
Edition 1st Edition
Languages
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Authors (2):
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Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
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Toc

Table of Contents (15) Chapters Close

Preface 1. TensorFlow - An Introduction 2. Regression FREE CHAPTER 3. Neural Networks - Perceptron 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Recurrent Neural Networks 7. Unsupervised Learning 8. Autoencoders 9. Reinforcement Learning 10. Mobile Computation 11. Generative Models and CapsNet 12. Distributed TensorFlow and Cloud Deep Learning 13. Learning to Learn with AutoML (Meta-Learning) 14. TensorFlow Processing Units

Learning to predict future Bitcoin value with RNNs

In this recipe, we will learn how to predict future Bitcoin value with an RNN. The key idea is that the temporal sequence of values observed in the past is a good predictor of future values. For this recipe, we will use the code available at https://github.com/guillaume-chevalier/seq2seq-signal-prediction under the MIT license. The Bitcoin value for a given temporal interval is downloaded via an API from https://www.coindesk.com/api/ . Here is a piece of the API documentation:

We offer historical data from our Bitcoin Price Index through the following endpoint:
https://api.coindesk.com/v1/bpi/historical/close.json
By default, this will return the previous 31 days' worth of data. This endpoint accepts the following optional parameters:
?index=[USD/CNY]The index to return data for. Defaults to USD.
?currency=<VALUE...
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