December 2017
Intermediate to advanced
536 pages
14h 23m
English
Content preview from TensorFlow 1.x Deep Learning Cookbook
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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.jsonBy default, this will return the previous 31 days' worth of data. This endpoint ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.