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Deep Learning Quick Reference

You're reading from   Deep Learning Quick Reference Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Length 272 pages
Edition 1st Edition
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Author (1):
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Mike Bernico Mike Bernico
Author Profile Icon Mike Bernico
Mike Bernico
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Table of Contents (15) Chapters Close

Preface 1. The Building Blocks of Deep Learning FREE CHAPTER 2. Using Deep Learning to Solve Regression Problems 3. Monitoring Network Training Using TensorBoard 4. Using Deep Learning to Solve Binary Classification Problems 5. Using Keras to Solve Multiclass Classification Problems 6. Hyperparameter Optimization 7. Training a CNN from Scratch 8. Transfer Learning with Pretrained CNNs 9. Training an RNN from scratch 10. Training LSTMs with Word Embeddings from Scratch 11. Training Seq2Seq Models 12. Using Deep Reinforcement Learning 13. Generative Adversarial Networks 14. Other Books You May Enjoy

Using an LSTM for time series prediction

In this chapter, we're going to predict the minute-to-minute value of bitcoin in US dollars during the month of June 2017 by using the minute-to-minute price of bitcoin from January to May of 2017. I know this sounds really lucrative but before you buy that boat, I recommend reading through to the end of the chapter; this is something easier said and even easier modeled, than done.

Even if we were able to create the potential for an arbitrage (a difference in price between two markets due to an inefficiency) between USD and bitcoins using some model like this one, developing a trading strategy around bitcoin can be extremely complex because of the delay in finalizing bitcoin transactions. At the time of this writing, the average transaction time for a bitcoin transaction is over an hour! This "illiquidity" should be a consideration...
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