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

The perceptron

The perceptron was the name that Frank Rosenblatt gave to the first neural model in 1957. A perceptron is a neural network with a single layer of input linear neurons, followed by an output unit based on the sign(x) function (alternatively, it's possible to consider a bipolar unit whose output is -1 and 1). The architecture of a perceptron is shown in the following diagram:

Structure of a perceptron

Even though the diagram might appear quite complex, a perceptron can be summarized by the following equation:

All the vectors are conventionally column-vectors; therefore, the dot product transforms the input into a scalar, then the bias is added, and the binary output is obtained using the step function, which outputs 1 when z > 0 and 0 otherwise. At this point, a reader could object that the step function is non-linear; however, a non-linearity applied to the output layer is only a filtering operation that has no effect on the actual...