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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
Author Profile Icon Prateek Joshi
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

Creating a vector quantizer

You can use neural networks for vector quantization as well. Vector quantization is the N-dimensional version of rounding off. This is very commonly used across multiple areas in computer vision, NLP, and machine learning in general.

Getting ready

In previous recipes, we have already addressed vector quantization concepts: Compressing an image using vector quantization and Creating features using visual Codebook and vector quantization. In this recipe, we will define a neural network with two layers—10 neurons in input layer and 4 neurons in the output layer. Then we will use this network to divide the space into four regions.

Before starting, you need to make a change to fix a library bug...

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