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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

Training and fine-tuning pretrained deep learning models in MATLAB

Transfer learning is a machine learning approach wherein a model created for a particular task is repurposed as the initial foundation for a model addressing a second task. This technique entails leveraging knowledge acquired from one problem and applying it to a distinct yet related problem. Transfer learning is particularly useful in deep learning and neural networks, where pretrained models can be fine-tuned or used as feature extractors for new tasks.

In pretrained models, you start with a pretrained model that has been trained on a large dataset for a specific task, such as image classification, natural language processing, or speech recognition. These pretrained models are often complex neural networks with many layers. In many cases, you can use the layers of the pretrained model as feature extractors. You remove the final classification layer(s) and use the activations from the earlier layers as features...

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