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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Index

Deep convolutional networks

In the previous chapter, we saw how a multi-layer perceptron can achieve very high accuracy when working with an image dataset that is not very complex, such as the MNIST handwritten digits dataset. However, as the fully-connected layers are horizontal, the images, which in general are three-dimensional structures (widthheight x channels), must be flattened and transformed into one-dimensional arrays where the geometric properties are definitively lost.

With more complex datasets, where the distinction between classes depends on details and on their relationships, this approach can yield moderate accuracy, but it can never reach the precision required by production-ready applications.

The conjunction of neuroscientific studies and image processing techniques suggested experimenting with neural networks where the first layers work with bidimensional structures (without the channels), trying to extract a hierarchy of features...