Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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27
Index

Summary

In this chapter, we analyzed the role of momentum and how it's possible to manage adaptive corrections using RMSProp. Then, we combined momentum and RMSProp to derive a very powerful algorithm called Adam. In order to provide a complete picture, we also presented two slightly different adaptive algorithms, called AdaGrad and AdaDelta.

In the next sections, we discussed regularization methods and how they can be plugged into a Keras model. An important section was dedicated to a very diffused technique called dropout, which consists of setting to zero (dropping) a fixed percentage of samples through random selection. This method, although very simple, prevents the overfitting of very deep networks and encourages the exploration of different regions of the sample space, obtaining a result not very dissimilar to the ones analyzed in Chapter 15, Fundamentals of Ensemble Learning. The last topic was the batch normalization technique, which is a method for reducing the...