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Python Machine Learning Cookbook, - Second Edition

You're reading from  Python Machine Learning Cookbook, - Second Edition

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789808452
Pages 642 pages
Edition 2nd Edition
Languages
Authors (2):
Giuseppe Ciaburro Giuseppe Ciaburro
Profile icon Giuseppe Ciaburro
Prateek Joshi Prateek Joshi
Profile icon Prateek Joshi
View More author details
Toc

Table of Contents (18) Chapters close

Preface 1. The Realm of Supervised Learning 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

Introduction

Our brain is really good at identifying and recognizing things. We want machines to be able to do the same. A neural network is a framework that is modeled after the human brain to simulate our learning processes. Neural networks are designed to learn from data and recognize the underlying patterns. As with all learning algorithms, neural networks deal with numbers. Therefore, if we want to achieve any real-world task involving images, text, sensors, and so on, we have to convert them into a numerical format before we feed them into a neural network. We can use a neural network for classification, clustering, generation, and many other related tasks.

A neural network consists of layers of neurons. These neurons are modeled after the biological neurons in the human brain. Each layer is basically a set of independent neurons that are connected to the neurons on adjacent...

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