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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Index

Autoencoders

In the previous chapters (in particular, Chapter 3, Introduction to Semi-Supervised Learning and Chapter 4, Advanced Semi-Supervised Classification on semi-supervised learning), we discussed how real datasets are very often high-dimensional representations of samples that lie on low-dimensional manifolds (this is one of the semi-supervised pattern's assumptions, but it's generally true).

As the complexity of a model is proportional to the dimensionality of the input data, many techniques have been analyzed and optimized in order to reduce the actual number of valid components. For example, PCA selects features according to their relative explained variance, while ICA and generic dictionary learning techniques look for basic atoms that can be combined to rebuild the original samples. In this chapter, we're going to analyze a family of models based on a slightly different approach, but whose capabilities are dramatically increased by the...