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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

You're reading from   Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits A practical guide to implementing supervised and unsupervised machine learning algorithms in Python

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781838826048
Length 384 pages
Edition 1st Edition
Languages
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Author (1):
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Tarek Amr Tarek Amr
Author Profile Icon Tarek Amr
Tarek Amr
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Supervised Learning
2. Introduction to Machine Learning FREE CHAPTER 3. Making Decisions with Trees 4. Making Decisions with Linear Equations 5. Preparing Your Data 6. Image Processing with Nearest Neighbors 7. Classifying Text Using Naive Bayes 8. Section 2: Advanced Supervised Learning
9. Neural Networks – Here Comes Deep Learning 10. Ensembles – When One Model Is Not Enough 11. The Y is as Important as the X 12. Imbalanced Learning – Not Even 1% Win the Lottery 13. Section 3: Unsupervised Learning and More
14. Clustering – Making Sense of Unlabeled Data 15. Anomaly Detection – Finding Outliers in Data 16. Recommender System – Getting to Know Their Taste 17. Other Books You May Enjoy
Neural Networks – Here Comes Deep Learning

It is not uncommon to read news articles or encounter people who misuse the term deep learning in place of machine learning. This is due to the fact that this particular sub-field of machine learning has become very successful at solving plenty of previously unsolvable image processing and natural language processing problems. This success has caused many to confuse the child field with its parent.

The term deep learning refers to deep Artificial Neural Networks (ANNs). The latter concept comes in different forms and shapes. In this chapter, we are going to cover one subset of feedforward neural networks known as the Multilayer Perceptron (MLP). It is one of the most commonly used types and is implemented by scikit-learn. As its name suggests, it is composed of multiple layers, and it is a feedforward network as there are no cyclic connections between its layers. The more...

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