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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Unlock the power of deep learning for swift and enhanced results

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781835087695
Length 374 pages
Edition 2nd Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Getting Started with Matlab
2. Chapter 1: Exploring MATLAB for Machine Learning FREE CHAPTER 3. Chapter 2: Working with Data in MATLAB 4. Part 2: Understanding Machine Learning Algorithms in MATLAB
5. Chapter 3: Prediction Using Classification and Regression 6. Chapter 4: Clustering Analysis and Dimensionality Reduction 7. Chapter 5: Introducing Artificial Neural Network Modeling 8. Chapter 6: Deep Learning and Convolutional Neural Networks 9. Part 3: Machine Learning in Practice
10. Chapter 7: Natural Language Processing Using MATLAB 11. Chapter 8: MATLAB for Image Processing and Computer Vision 12. Chapter 9: Time Series Analysis and Forecasting with MATLAB 13. Chapter 10: MATLAB Tools for Recommender Systems 14. Chapter 11: Anomaly Detection in MATLAB 15. Index 16. Other Books You May Enjoy

Discovering pattern recognition using ANNs

Pattern recognition is a branch of machine learning and AI that focuses on the identification of patterns or regularities in data. It involves the automatic discovery and extraction of meaningful information from datasets, with the aim of categorizing or classifying data into different classes or groups. Overall, pattern recognition plays a crucial role in automating the process of identifying patterns and making decisions based on data, making it a fundamental component of many modern AI systems.

Pattern recognition offers numerous benefits in automating decision-making and handling complex data. However, it also poses challenges related to data quality, interpretability, and computational requirements. To leverage its advantages effectively, practitioners need to carefully design and train models while being aware of potential limitations and biases in the data and algorithms.

The classical approach to pattern recognition uses data...

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