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

Regularization and Dropout

Overfitting is a common issue in deep models. Their extremely high capacity can often become problematic even with very large datasets because the ability to learn the structure of the training set is not always related to the ability to generalize. A deep neural network can easily become an associative memory, but the final internal configuration might not be the most suitable to manage samples belonging to the same distribution because that distribution was never presented during the training process. It goes without saying that this behavior is proportional to the complexity of the separation hypersurface.

A linear classifier has a minimal chance of overfitting, and a polynomial classifier is incredibly more prone to do so. A combination of hundreds, thousands, or more non-linear functions yields a separation hypersurface that is beyond any possible analysis.

In 1991, Hornik (in Hornik K., Approximation Capabilities of Multilayer Feedforward...