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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Sanger's network

A Sanger's network is a neural network model for online principal component extraction, proposed by T. D. Sanger in Optimal Unsupervised Learning in Sanger T. D., Single-Layer Linear Feedforward Neural Network, Neural Networks, 1989/2. The author started with the standard version of Hebb's rule and modified it to be able to extract a variable number of principal components in descending order . The resulting approach, which is a natural extension of Oja's rule, has been called the Generalized Hebbian Rule (GHA)—you might also sometimes see it called Generalized Hebbian Learning (GHL). The structure of the network is represented in the following diagram:

Structure of a Sanger's Network

The network is fed with samples extracted from an n-dimensional dataset:

The m output neurons are connected to the input through a weight matrix, W = {wij}, where the first index refers to the input components (pre-synaptic units) and...