Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
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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 means ignoring a certain set of hidden nodes during the learning phase of...