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MATLAB for Machine Learning

MATLAB for Machine Learning

By : Giuseppe Ciaburro
4.8 (4)
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MATLAB for Machine Learning

MATLAB for Machine Learning

4.8 (4)
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)
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1
Part 1: Getting Started with Matlab
4
Part 2: Understanding Machine Learning Algorithms in MATLAB
9
Part 3: Machine Learning in Practice

Summary

In this chapter, we gained insight into simulating typical human brain activities using ANNs. We grasped the fundamental concepts behind ANNs, delving into the creation of a basic neural network architecture. This exploration encompassed elements such as input, hidden, and output layers, connection weights, and activation functions. Our understanding extended to crucial decisions regarding hidden layer count, node quantity within each layer, and network training algorithms.

Then we focused on data fitting and pattern recognition using neural networks. We engaged in script analysis to master the utilization of neural network functions via the command line. We then ventured into the Neural Network Toolbox, featuring algorithms, pre-trained models, and apps for crafting, training, visualizing, and simulating shallow and deep neural networks. The Neural Network Toolbox offers an accessible interface—the Neural Network getting started GUI—which serves as the launchpad...

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