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 have presented the concept of a deep convolutional network, which is a generic architecture that can be employed in any visual processing task. The idea is based on hierarchical information management, aimed at extracting features starting from low-level elements and moving forward to the high-level details that can be helpful to achieve specific goals.

We discussed the concept of convolution and how it's applied in discrete and finite samples. We followed on by defining the properties of standard convolution, before analyzing some important variants such as atrous (or dilated) convolution, separable (and depth wise separable) convolution, and, eventually, transpose convolution. All these methods can work with 1D, 2D, and 3D samples, even if the most diffused applications are based on bidimensional (not considering the channels) matrices representing static images. In the same section, we also discussed how pooling layers can be employed to reduce...