Matlab For Machine Learning

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MATLAB for Machine Learning

Giuseppe Ciaburro
MATLAB for Machine Learning Book PDF

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
Page : 374 pages
File Size : 49,8 Mb
Release : 2017-08-28
Category : Computers
ISBN : 9781788399395

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MATLAB for Machine Learning by Giuseppe Ciaburro Pdf

Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

MATLAB Deep Learning

Phil Kim
MATLAB Deep Learning Book PDF

Author : Phil Kim
Publisher : Apress
Page : 162 pages
File Size : 48,9 Mb
Release : 2017-06-15
Category : Computers
ISBN : 9781484228456

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MATLAB Deep Learning by Phil Kim Pdf

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

MATLAB Machine Learning

Michael Paluszek,Stephanie Thomas
MATLAB Machine Learning Book PDF

Author : Michael Paluszek,Stephanie Thomas
Publisher : Apress
Page : 335 pages
File Size : 49,6 Mb
Release : 2016-12-28
Category : Computers
ISBN : 9781484222508

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MATLAB Machine Learning by Michael Paluszek,Stephanie Thomas Pdf

This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results. Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology. The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book. What you'll learn: An overview of the field of machine learning Commercial and open source packages in MATLAB How to use MATLAB for programming and building machine learning applications MATLAB graphics for machine learning Practical real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning.

Practical MATLAB Deep Learning

Michael Paluszek,Stephanie Thomas
Practical MATLAB Deep Learning Book PDF

Author : Michael Paluszek,Stephanie Thomas
Publisher : Apress
Page : 260 pages
File Size : 50,7 Mb
Release : 2020-02-07
Category : Computers
ISBN : 9781484251249

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Practical MATLAB Deep Learning by Michael Paluszek,Stephanie Thomas Pdf

Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. You’ll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images. Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. What You Will LearnExplore deep learning using MATLAB and compare it to algorithmsWrite a deep learning function in MATLAB and train it with examplesUse MATLAB toolboxes related to deep learningImplement tokamak disruption predictionWho This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.

A First Course in Machine Learning

Simon Rogers,Mark Girolami
A First Course in Machine Learning Book PDF

Author : Simon Rogers,Mark Girolami
Publisher : CRC Press
Page : 428 pages
File Size : 42,9 Mb
Release : 2016-10-14
Category : Computers
ISBN : 9781498738545

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A First Course in Machine Learning by Simon Rogers,Mark Girolami Pdf

Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/"

MATLAB Machine Learning Recipes

Michael Paluszek,Stephanie Thomas
MATLAB Machine Learning Recipes Book PDF

Author : Michael Paluszek,Stephanie Thomas
Publisher : Apress
Page : 358 pages
File Size : 53,7 Mb
Release : 2019-01-31
Category : Computers
ISBN : 9781484239162

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MATLAB Machine Learning Recipes by Michael Paluszek,Stephanie Thomas Pdf

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What you'll learn:How to write code for machine learning, adaptive control and estimation using MATLAB How these three areas complement each other How these three areas are needed for robust machine learning applications How to use MATLAB graphics and visualization tools for machine learning How to code real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.

Computational Methods for Deep Learning

Wei Qi Yan
Computational Methods for Deep Learning Book PDF

Author : Wei Qi Yan
Publisher : Springer Nature
Page : 134 pages
File Size : 53,6 Mb
Release : 2020-12-04
Category : Computers
ISBN : 9783030610814

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Computational Methods for Deep Learning by Wei Qi Yan Pdf

Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations. Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms. As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers. This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision. Dr. Wei Qi Yan is an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title, Visual Cryptography for Image Processing and Security.

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Abdulhamit Subasi
Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques Book PDF

Author : Abdulhamit Subasi
Publisher : Academic Press
Page : 456 pages
File Size : 46,6 Mb
Release : 2019-03-16
Category : Business & Economics
ISBN : 9780128176733

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques by Abdulhamit Subasi Pdf

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis. Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction Explains how to apply machine learning techniques to EEG, ECG and EMG signals Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series

Data-Driven Science and Engineering

Steven L. Brunton,J. Nathan Kutz
Data Driven Science and Engineering Book PDF

Author : Steven L. Brunton,J. Nathan Kutz
Publisher : Cambridge University Press
Page : 615 pages
File Size : 46,9 Mb
Release : 2022-05-05
Category : Computers
ISBN : 9781009098489

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Data-Driven Science and Engineering by Steven L. Brunton,J. Nathan Kutz Pdf

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES

Cesar Perez Lopez
DEEP LEARNING with MATLAB NEURAL NETWORKS by EXAMPLES Book PDF

Author : Cesar Perez Lopez
Publisher : CESAR PEREZ
Page : 154 pages
File Size : 46,9 Mb
Release : 2020-09-13
Category : Computers
ISBN : 9781716584848

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DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES by Cesar Perez Lopez Pdf

MATLAB has the tool Deep Learning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets (Big data), you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.

MATLAB Recipes

Michael Paluszek,Stephanie Thomas
MATLAB Recipes Book PDF

Author : Michael Paluszek,Stephanie Thomas
Publisher : Apress
Page : 314 pages
File Size : 42,7 Mb
Release : 2015-11-23
Category : Computers
ISBN : 9781484205594

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MATLAB Recipes by Michael Paluszek,Stephanie Thomas Pdf

Learn from state-of-the-art examples in robotics, motors, detection filters, chemical processes, aircraft, and spacecraft. This is a practical reference for industry engineers using MATLAB to solve everyday problems. With MATLAB Recipes: A Problem-Solution Approach you will review contemporary MATLAB coding including the latest language features and use MATLAB as a software development environment including code organization, GUI development, and algorithm design and testing. This book provides practical guidance for using MATLAB to build a body of code you can turn to time and again for solving technical problems in your line of work. Develop algorithms, test them, visualize the results, and pass the code along to others to create a functional code base for your firm.

Probabilistic Machine Learning

Kevin P. Murphy
Probabilistic Machine Learning Book PDF

Author : Kevin P. Murphy
Publisher : MIT Press
Page : 858 pages
File Size : 55,9 Mb
Release : 2022-03-01
Category : Computers
ISBN : 9780262369305

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Probabilistic Machine Learning by Kevin P. Murphy Pdf

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Statistics in MATLAB

MoonJung Cho,Wendy L. Martinez
Statistics in MATLAB Book PDF

Author : MoonJung Cho,Wendy L. Martinez
Publisher : CRC Press
Page : 280 pages
File Size : 44,8 Mb
Release : 2014-12-15
Category : Business & Economics
ISBN : 9781466596573

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Statistics in MATLAB by MoonJung Cho,Wendy L. Martinez Pdf

This primer provides an accessible introduction to MATLAB version 8 and its extensive functionality for statistics. Fulfilling the need for a practical user's guide, the book covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB, presenting examples of how MATLAB can be used to analyze data. It explains how to determine what method should be used for analysis, and includes figures, visual aids, and access to a companion website with data sets and additional examples.

Machine Component Analysis with MATLAB

Dan B. Marghitu,Mihai Dupac
Machine Component Analysis with MATLAB Book PDF

Author : Dan B. Marghitu,Mihai Dupac
Publisher : Butterworth-Heinemann
Page : 232 pages
File Size : 44,8 Mb
Release : 2019-03-15
Category : Science
ISBN : 9780128042298

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Machine Component Analysis with MATLAB by Dan B. Marghitu,Mihai Dupac Pdf

Machine Design Analysis with MATLAB is a highly practical guide to the fundamental principles of machine design which covers the static and dynamic behavior of engineering structures and components. MATLAB has transformed the way calculations are made for engineering problems by computationally generating analytical calculations, as well as providing numerical calculations. Using step-by-step, real world example problems, this book demonstrates how you can use symbolic and numerical MATLAB as a tool to solve problems in machine design. This book provides a thorough, rigorous presentation of machine design, augmented with proven learning techniques which can be used by students and practicing engineers alike. Comprehensive coverage of the fundamental principles in machine design Uses symbolical and numerical MATLAB calculations to enhance understanding and reinforce learning Includes well-designed real-world problems and solutions

Pattern Recognition and Computational Intelligence Techniques Using Matlab

E. S. Gopi
Pattern Recognition and Computational Intelligence Techniques Using Matlab Book PDF

Author : E. S. Gopi
Publisher : Springer Nature
Page : 256 pages
File Size : 55,9 Mb
Release : 2019-10-17
Category : Technology & Engineering
ISBN : 9783030222734

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Pattern Recognition and Computational Intelligence Techniques Using Matlab by E. S. Gopi Pdf

This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way, using Matlab to illustrate topics and concepts. The author covers computational intelligence tools like particle swarm optimization, bacterial foraging, simulated annealing, genetic algorithm, and artificial neural networks. The Matlab based illustrations along with the code are given for every topic. Readers get a quick basic understanding of various pattern recognition techniques using only the required depth in math. The Matlab program and algorithm are given along with the running text, providing clarity and usefulness of the various techniques. Presents pattern recognition and the computational intelligence using Matlab; Includes mixtures of theory, math, and algorithms, letting readers understand the concepts quickly; Outlines an array of classifiers, various regression models, statistical tests and the techniques for pattern recognition using computational intelligence.