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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

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781835087695

Length 374 pages

Edition 2nd Edition

Languages

MATLAB

Tools

MATLAB

Concepts

Deep Learning

Author (1):

Giuseppe Ciaburro

View More author details

Table of Contents (17) Chapters

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

Why subscribe?

16. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

accuracy

improving, with Random Forest algorithm 326- 328

activation function 141, 143

exponential linear unit (ELU) function 142

hyperbolic tangent (Tanh) function 142

rectified linear unit (ReLU) function 142

sigmoid function 142

softmax function 142

step function 142

Adam optimization

exploring 175

Adaptive Boosting (AdaBoost) 232, 306

adaptive moment estimation (Adam) 174

Adaptive Synthetic Sampling (ADASYN) 289

adjusted R-squared 81

advanced data preprocessing techniques 52

correlation analysis 54-58

data normalization for feature scaling 53

advanced optimization techniques 171

Adam optimization, exploring 175

exploring 171

second-order optimization methods 175, 176

stochastic gradient descent (SGD) 172-174

advanced regularization...

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Authors (1)

Giuseppe Ciaburro

Giuseppe Ciaburro

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).

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