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Python Machine Learning Cookbook

You're reading from   Python Machine Learning Cookbook Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789808452
Length 642 pages
Edition 2nd Edition
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Authors (2):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
Prateek Joshi Prateek Joshi
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Prateek Joshi
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Toc

Table of Contents (18) Chapters Close

Preface 1. The Realm of Supervised Learning FREE CHAPTER 2. Constructing a Classifier 3. Predictive Modeling 4. Clustering with Unsupervised Learning 5. Visualizing Data 6. Building Recommendation Engines 7. Analyzing Text Data 8. Speech Recognition 9. Dissecting Time Series and Sequential Data 10. Analyzing Image Content 11. Biometric Face Recognition 12. Reinforcement Learning Techniques 13. Deep Neural Networks 14. Unsupervised Representation Learning 15. Automated Machine Learning and Transfer Learning 16. Unlocking Production Issues 17. Other Books You May Enjoy

Building a recurrent neural network for sequential data analysis

Recurrent neural networks are really good at analyzing sequential and time-series data. A recurrent neural network (RNN) is a neural model in which a bidirectional flow of information is present. In other words, while the propagation of signals in feedforward networks takes place only in a continuous manner, going from inputs to outputs, recurrent networks are different. In them, this propagation can also occur from a neural layer following a previous one, or between neurons belonging to the same layer, and even between a neuron and itself.

Getting ready

When we deal with sequential and time-series data, we cannot just extend generic models. The temporal dependencies...

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