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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The back-propagation algorithm

This algorithm is more of a methodology than an actual algorithm. In fact, it was designed to be flexible enough to adapt to any kind of neural architecture without any substantial changes. Therefore, in this section we'll define the main concepts without focusing on a particular case. Those who are interested in implementing it will be able to apply the same techniques to different kinds of networks with minimal effort (assuming that all requirements are met).

The goal of a training process using a deep learning model is normally achieved by minimizing a cost function. Let's suppose we have a network parameterized with a global vector . In that case, the cost function (using the same notation for loss and cost but with different parameters to disambiguate) is defined as follows:

We've already explained that the minimization of the previous expression (which is the empirical risk) is a way to minimize the real expected risk and...