March 2018
Intermediate to advanced
484 pages
10h 31m
English
book
Deep Learning with TensorFlow - Second Edition
by Giancarlo Zaccone, Vihan Jain, Md. Rezaul Karim, Motaz Saad
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Developing a predictive model for time series data
RNNs, specifically LSTM models, is often a difficult topic to understand. Time series prediction is a useful application of RNNs because of temporal dependencies in the data. Time series data is abundantly available online. In this section, we will see an example of using an LSTM for handling time series data. Our LSTM network will be able to predict the number of airline passengers in the future.
Description of the dataset
The dataset that I will be using is data about international airline passengers from 1949 to 1960. The dataset can be downloaded from https://datamarket.com/data/set/22u3/international-airlinepassengers- monthly-totals-in#!ds=22u3&display=line. The following screenshot shows ...
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.