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)
26
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27
Index

Summary

In this chapter, we started our exploration of the deep learning world by introducing the basic concepts that led the first researchers to improve algorithms until they achieved the top results we can achieve nowadays. The first part explained the structure of a basic artificial neuron, which combines a linear operation followed by an optional non-linear scalar function. A single layer of linear neurons was initially proposed as the first neural network, with the name of the perceptron.

Even though it was quite powerful for many problems, this model soon showed its limitations when working with non-linear separable datasets. A perceptron is not very different from logistic regression, and there's no concrete reason to employ it. Nevertheless, this model opened the doors to a family of extremely powerful models obtained by combining multiple non-linear layers. The multilayer perceptron, which has been proven to be a universal approximator, is able to manage almost any...