Book Image

Mastering Machine Learning Algorithms

Book Image

Mastering Machine Learning Algorithms

Overview of this book

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn v0.19.1. You will also learn how to use Keras and TensorFlow 1.x to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.
Table of Contents (22 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
13
Deep Belief Networks
Index

Label propagation


Label propagation is a family of semi-supervised algorithms based on a graph representation of the dataset. In particular, if we have N labeled points (with bipolar labels +1 and -1) and M unlabeled points (denoted by y=0), it's possible to build an undirected graph based on a measure of geometric affinity among samples. If G = {V, E} is the formal definition of the graph, the set of vertices is made up of sample labels V = { -1, +1, 0 }, while the edge set is based on an affinity matrixW (often called adjacency matrix when the graph is unweighted), which depends only on the X values, not on the labels.

In the following graph, there's an example of such a structure:

Example of binary graph

In the preceding example graph, there are four labeled points (two with y=+1 and two with y=-1), and two unlabeled points (y=0). The affinity matrix is normally symmetric and square with dimensions equal to (N+M) x (N+M). It can be obtained with different approaches. The most common ones...