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 presented autoencoders as unsupervised models that can learn to represent high-dimensional datasets with lower-dimensional code. They are structured into two separate blocks (which, however, are trained together): an encoder, responsible for mapping the input sample to an internal representation, and a decoder, which must perform the inverse operation, rebuilding the original image starting from the code.

We have also discussed how autoencoders can be used to denoise samples and how it's possible to impose a sparsity constraint on the code layer to resemble the concept of standard dictionary learning. The last topic was about a slightly different pattern called a VAE. The idea is to build a generative model that is able to reproduce all the possible samples belonging to a training distribution.

In the next chapter, we are going to briefly introduce a very important model family called generative adversarial networks (GANs), which are...