Book Image

MATLAB for Machine Learning - Second Edition

By : Giuseppe Ciaburro
Book Image

MATLAB for Machine Learning - Second Edition

By: Giuseppe Ciaburro

Overview of this book

Discover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you’ll learn how to seamlessly interact with the workspace. You’ll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you’ll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You’ll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you’ll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you’ll be able to put it all together by applying major machine learning algorithms in real-world scenarios.
Table of Contents (17 chapters)
Free Chapter
1
Part 1: Getting Started with Matlab
4
Part 2: Understanding Machine Learning Algorithms in MATLAB
9
Part 3: Machine Learning in Practice

Approaching CNNs

As outlined in Chapter 5, Introducing Artificial Neural Networks Modeling, ANNs draw their inspiration from biological NNs. These ANNs aim to replicate human cognitive processes by emulating the mechanisms observed in natural NNs. They serve the purpose of estimating or approximating functions that might rely on numerous inputs, many of which could be unfamiliar. ANNs are typically conceptualized as networks of interconnected neurons, facilitating the exchange of messages. Each connection possesses an associated weight, the value of which can be adjusted through learning from experience. This adaptive characteristic empowers NNs to accommodate diverse input types and facilitates their capacity to learn (Figure 6.1):

Figure 6.1 – ANN architecture with hidden layers

Figure 6.1 – ANN architecture with hidden layers

ANNs define a neuron as the central processing unit (CPU) that executes a mathematical operation to produce a single output from a set of input values. The neuron’...