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

Exploring advanced optimization techniques

Advanced optimization techniques are powerful methods used to enhance the efficiency and effectiveness of optimization algorithms. These techniques aim to overcome the limitations of traditional optimization approaches, particularly in complex, high-dimensional, or non-convex optimization problems.

In machine learning, advanced optimization techniques are essential for training complex models effectively, improving convergence, avoiding overfitting, and handling high-dimensional data. In the following subsection, some advanced optimization techniques commonly used in machine learning are listed.

Understanding SGD

SGD is a popular and fundamental optimization algorithm used in machine learning for training models, especially in large-scale and complex settings. It’s a variant of the traditional gradient descent method designed to address efficiency and convergence issues when dealing with large datasets. The stochastic aspect...