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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Hebb's rule

Hebb's rule has been proposed as a conjecture in 1949 by the Canadian psychologist Donald Hebb to describe the synaptic plasticity of natural neurons.

A few years after its publication, this rule was confirmed by neurophysiological studies, and many research studies have also shown its validity in many applications of artificial intelligence. Before introducing the rule, it's useful to describe the generic Hebbian neuron, as shown in the following diagram:

Generic Hebbian neuron with a vectorial input

The neuron is a simple computational unit that receives an input vector , from the pre-synaptic units (other neurons or perceptive systems) and outputs a single scalar value, y. The internal structure of the neuron is represented by a weight vector, , that models the strength of each synapse. For a single multi-dimensional input, the output is obtained as follows:

In this model, we are assuming that each input signal is encoded in the...