MATLAB for Machine Learning: Unlock the power of deep learning for swift and enhanced results - Softcover

Ciaburro, Giuseppe

Softcover

ISBN 10: 1835087698 ISBN 13: 9781835087695

Verlag: Packt Publishing, 2024

Inhaltsangabe

Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applications

Key Features

• Work with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithms
• Evaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoring
• Uncover effective approaches to deep learning for computer vision, time series analysis, and forecasting
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

• Discover different ways to transform data into valuable insights
• Explore the different types of regression techniques
• Grasp the basics of classification through Naive Bayes and decision trees
• Use clustering to group data based on similarity measures
• Perform data fitting, pattern recognition, and cluster analysis
• Implement feature selection and extraction for dimensionality reduction
• Harness MATLAB tools for deep learning exploration

Who this book is for

This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.

Table of Contents

1. Exploring MATLAB for Machine Learning
2. Working with Data in MATLAB
3. Prediction Using Classification and Regression
4. Clustering Analysis and Dimensionality Reduction
5. Introducing Artificial Neural Networks Modeling
6. Deep Learning and Convolutional Neural Networks
7. Natural Language Processing Using MATLAB
8. MATLAB for Image Processing and Computer Vision
9. Time Series Analysis and Forecasting with MATLAB
10. MATLAB Tools for Recommender Systems
11. Anomaly Detection in MATLAB

Über diesen Titel

Über die Autorin bzw. den Autor

Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).

Details zur Ausgabe

• Verlag: Packt Publishing
• Erscheinungsdatum: 2024
• Sprache: Englisch
• ISBN 10: 1835087698
• ISBN 13: 9781835087695
• Einband: Tapa blanda
• Auflage: 2
• Anzahl der Seiten: 374
• Kontakt zum Hersteller: Nicht verfügbar