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Python Machine Learning

You're reading from   Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789955750
Length 772 pages
Edition 3rd Edition
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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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Toc

Table of Contents (21) 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 Datasets – 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 17. Generative Adversarial Networks for Synthesizing New Data 18. Reinforcement Learning for Decision Making in Complex Environments 19. Other Books You May Enjoy 20. Index

Modeling Sequential Data Using Recurrent Neural Networks

In the previous chapter, we focused on convolutional neural networks (CNNs). We covered the building blocks of CNN architectures and how to implement deep CNNs in TensorFlow. Finally, you learned how to use CNNs for image classification. In this chapter, we will explore recurrent neural networks (RNNs) and see their application in modeling sequential data.

We will cover the following topics:

  • Introducing sequential data
  • RNNs for modeling sequences
  • Long short-term memory (LSTM)
  • Truncated backpropagation through time (TBPTT)
  • 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 Jules Verne's The Mysterious Island
  • Using gradient clipping to avoid exploding gradients
  • Introducing the Transformer model and understanding...
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