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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27
Index

K-means

When we discussed the Gaussian mixture algorithm, we defined it as soft K-means. The reason is that each cluster was represented by three elements: mean, variance, and weight. Each sample always belongs to all clusters with a probability provided by the Gaussian distributions. This approach can be very useful when it's possible to manage the probabilities as weights, but in many other situations, it's preferable to determine a single cluster per sample.

Such an approach is called hard clustering and K-means can be considered the hard version of a Gaussian mixture. In fact, when all variances , the distributions degenerate to Dirac deltas , which represent perfect spikes centered at a specific point (even if they are not real functions but distributions). In this scenario, the only possibility to determine the most appropriate cluster is to find the shortest distance between a sample point and all the centers (from now on, we are going to call them centroids...