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

Rubner-Tavan's network


In Chapter 5EM Algorithm and Applications, we said that any algorithm that decorrelates the input covariance matrix is performing a PCA without dimensionality reduction. Starting from this approach, Rubner, and Tavan (in the paper A Self-Organizing Network for Principal-Components Analysis, Rubner J., Tavan P., Europhysics. Letters, 10(7), 1989) proposed a neural model whose goal is decorrelating the output components to force the consequent decorrelation of the output covariance matrix (in lower-dimensional subspace). Assuming a zero-centered dataset and E[y] = 0, the output covariance matrix for m principal components is as follows:

Hence, it's possible to achieve an approximate decorrelation, forcing the terms yiyj with i ≠ j to become close to zero. The main difference with a standard approach (such as whitening or vanilla PCA) is that this procedure is local, while all the standard methods operate globally, directly with the covariance matrix. The neural model...