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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

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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