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Deep Learning with TensorFlow

Deep Learning with TensorFlow

By : Zaccone, Karim
3 (4)
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Deep Learning with TensorFlow

Deep Learning with TensorFlow

3 (4)
By: Zaccone, Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (13 chapters)
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12
Index

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 the metadata of the international airline passengers:

Figure 14: Metadata of the international airline passengers (source: https://datamarket.com/)

You can download the data by choosing the Export tab and then selecting CSV (,) in the Export group...

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