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

Summary


In this chapter, we have discussed Hebb's rule, showing how it can drive the computation of the first principal component of the input dataset. We have also seen that this rule is unstable because it leads to the infinite growth of the synaptic weights and how it's possible to solve this problem using normalization or Oja's rule. 

We have introduced two different neural networks based on Hebbian learning (Sanger's and Rubner-Tavan's networks), whose internal dynamics are slightly different, which are able to extract the first n principal components in the right order (starting from the largest eigenvalue) without eigendecomposing the input covariance matrix.

Finally, we have introduced the concept of SOM and presented a model called a Kohonen network, which is able to map the input patterns onto a surface where some attractors (one per class) are placed through a competitive learning process. Such a model is able to recognize new patterns (belonging to the same distribution) by eliciting...