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You're reading from Mastering TensorFlow 1.x Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781788292061

Length 474 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Table of Contents (26) Chapters

Title Page

Packt Upsell

Foreword

Contributors

Preface

1. TensorFlow 101 FREE CHAPTER

2. High-Level Libraries for TensorFlow

3. Keras 101

4. Classical Machine Learning with TensorFlow

5. Neural Networks and MLP with TensorFlow and Keras

6. RNN with TensorFlow and Keras

7. RNN for Time Series Data with TensorFlow and Keras

8. RNN for Text Data with TensorFlow and Keras

9. CNN with TensorFlow and Keras

10. Autoencoder with TensorFlow and Keras

11. TensorFlow Models in Production with TF Serving

12. Transfer Learning and Pre-Trained Models

13. Deep Reinforcement Learning

14. Generative Adversarial Networks

15. Distributed Models with TensorFlow Clusters

16. TensorFlow Models on Mobile and Embedded Platforms

17. TensorFlow and Keras in R

18. Debugging TensorFlow Models

19. Tensor Processing Units

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Airline Passengers dataset

For the sake of brevity and simplicity, we selected a very small dataset called International Airline Passengers (airpass). The data contains the number of total passengers every month, from January 1949 to December 1960. The numbers in the dataset refer to the amount in thousands. This dataset was originally used by Box and Jenkins in their work in 1976. It was collected as part of TimeSeries Dataset Library (TSDL) along with various other time series datasets by Professor Rob Hyndman at Monash University, Australia. Later, the TSDL was moved to DataMarket (http://datamarket.com).

Note

You can download the dataset from the following link:https://datamarket.com/data/set/22u3/international-airline-passengers-monthly-totals-in-thousands-jan-49-dec-60.

Loading the airpass dataset

We keep the dataset as a CSV file in the ts-data folder in the datasets root (~/datasets) and use the following commands to load the data in a pandas data frame: