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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

Deep Learning and Convolutional Neural Networks

Deep learning (DL) is a machine learning (ML) technology based on multilayer artificial neural networks (ANNs) that has allowed many applications to reach a high degree of accuracy. Deep NNs (DNNs) are capable of modeling and have the ability to capture intricate connections between input and output information. Among the highly effective uses, computer vision (CV) stands out, encompassing activities such as categorization, image regression, and the identification of objects. As an illustration, an advanced NN can produce a stratified portrayal of entities, wherein each entity is recognized by a collection of features taking the shape of visual basics, such as specific contours, directed lines, surface details, and repetitive designs. Convolutional networks (CNNs) are characterized by convolutional layers, which use filters to analyze data in a local region and produce an activation map. These activation maps are then processed by pooling...

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