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Python Machine Learning by Example

You're reading from   Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781800209718
Length 526 pages
Edition 3rd Edition
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Author (1):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Table of Contents (17) Chapters Close

Preface 1. Getting Started with Machine Learning and Python 2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER 3. Recognizing Faces with Support Vector Machine 4. Predicting Online Ad Click-Through with Tree-Based Algorithms 5. Predicting Online Ad Click-Through with Logistic Regression 6. Scaling Up Prediction to Terabyte Click Logs 7. Predicting Stock Prices with Regression Algorithms 8. Predicting Stock Prices with Artificial Neural Networks 9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques 10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling 11. Machine Learning Best Practices 12. Categorizing Images of Clothing with Convolutional Neural Networks 13. Making Predictions with Sequences Using Recurrent Neural Networks 14. Making Decisions in Complex Environments with Reinforcement Learning 15. Other Books You May Enjoy
16. Index

Preventing overfitting in neural networks

A neural network is powerful as it can derive hierarchical features from data with the right architecture (the right number of hidden layers and hidden nodes). It offers a great deal of flexibility and can fit a complex dataset. However, this advantage will become a weakness if the network is not given enough control over the learning process. Specifically, it may lead to overfitting if a network is only good at fitting to the training set but is not able to generalize to unseen data. Hence, preventing overfitting is essential to the success of a neural network model.

There are mainly three ways to impose restrictions on our neural networks: L1/L2 regularization, dropout, and early stopping. We practiced the first method in Chapter 5, Predicting Online Ad Click-Through with Logistic Regression, and will discuss another two in this section.

Dropout

Dropout means ignoring a certain set of hidden nodes during the learning phase of...

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