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Python Machine Learning, Second Edition

You're reading from   Python Machine Learning, Second Edition Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787125933
Length 622 pages
Edition 2nd Edition
Languages
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Authors (2):
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Vahid Mirjalili Vahid Mirjalili
Author Profile Icon Vahid Mirjalili
Vahid Mirjalili
Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
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Table of Contents (18) Chapters Close

Preface 1. Giving Computers the Ability to Learn from Data 2. Training Simple Machine Learning Algorithms for Classification FREE CHAPTER 3. A Tour of Machine Learning Classifiers Using scikit-learn 4. Building Good Training Sets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Implementing a Multilayer Artificial Neural Network from Scratch 13. Parallelizing Neural Network Training with TensorFlow 14. Going Deeper – The Mechanics of TensorFlow 15. Classifying Images with Deep Convolutional Neural Networks 16. Modeling Sequential Data Using Recurrent Neural Networks Index

Chapter 16. Modeling Sequential Data Using Recurrent Neural Networks

In the previous chapter, we focused on Convolutional Neural Networks (CNNs) for image classification. In this chapter, we will explore Recurrent Neural Networks (RNNs) and see their application in modeling sequential data and a specific subset of sequential data—time-series data. As an overview, in this chapter, we will cover the following topics:

  • Introducing sequential data
  • RNNs for modeling sequences
  • Long Short-Term Memory (LSTM)
  • Truncated Backpropagation Through Time (T-BPTT)
  • Implementing a multilayer RNN for sequence modeling in TensorFlow
  • Project one – RNN sentiment analysis of the IMDb movie review dataset
  • Project two – RNN character-level language modeling with LSTM cells, using text data from Shakespeare's Hamlet
  • Using gradient clipping to avoid exploding gradients

Since this chapter is the last in our Python Machine Learning journey, we'll conclude with a summary of what we&apos...

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