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Mastering TensorFlow 1.x

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

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788292061
Length 474 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (21) Chapters Close

Preface 1. TensorFlow 101 2. High-Level Libraries for TensorFlow FREE CHAPTER 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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RNN for Time Series Data with TensorFlow and Keras

Time series data is a sequence of values, recorded or measured at different time intervals. Being a sequence, the RNN architecture is the best method to train models from such data. In this chapter, we will use a sample time series data set to showcase how to use TensorFlow and Keras to build RNN models.

We will cover the following topics in this chapter:

  • Airline passengers time series dataset:
    • Description and downloading of the dataset
    • Visualizing the dataset
  • Preprocessing the dataset for RNN in TensorFlow
  • RNN in TensorFlow for time series data:
    • SimpleRNN in TensorFlow
    • LSTM in TensorFlow
    • GRU in TensorFlow
  • Preprocessing the dataset for RNN in Keras
  • RNN in Keras for time series data:
    • SimpleRNN in Keras
    • LSTM in Keras
    • GRU in Keras

Let's start by learning about the sample dataset.

You can follow along with the code...
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