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)
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Index

Autoencoders

In this chapter, we're going to look at an unsupervised model family whose performance has been boosted by modern deep learning techniques. Autoencoders offer a different approach to classic problems such as dimensionality reduction or dictionary learning; however, unlike many other algorithms, they don't suffer the capacity limitations that affect many famous models. Moreover, they can exploit specific neural layers (such as convolutions) to extract pieces of information based on specialized criteria. In this way, the internal representations can be more robust to different kinds of distortion, and much more efficient in terms of the amount of information they can process.

In particular, we will discuss the following:

  • Standard autoencoders
  • Denoising autoencoders
  • Sparse autoencoders
  • Variational autoencoders

We can now start discussing the main concepts of autoencoders, focusing on the structural components and their features...