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

Denoising autoencoders

Autoencoders can be used to determine under-complete representations of a dataset. However, Bengio et al. (in Vincent P., Larochelle H., Lajoie I., Bengio Y., Manzagol P., Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion, from the Journal of Machine Learning Research, 11/2010) proposed using autoencoders to denoise the input samples rather than learning the exact representation of a sample in order to rebuild it from low-dimensional code.

This is not a brand-new idea, because, for example, Hopfield networks (proposed a few decades ago) had the same purpose, but their limitations in terms of capacity led researchers to look for different methods. Nowadays, deep autoencoders can easily manage high-dimensional data (such as images) with a consequent space requirement. That's why many people are now reconsidering the idea of teaching a network how to rebuild a sample image starting from...