These are sets of specific functions that provided more specialized functionality. Matlab’s functionality can be considerably expanded by the addition of toolboxes. Users can plot their data very simply, and then modify colours, sizes, scales, etc, by handling the graphical interactive tools. The graphical output is optimized for communication. Vectorized operations such as adding two arrays together need only one command, instead of a for or while loop. For instance, cross-products, dot-products, determinants, inverse matrices. Different mathematical methods that work on arrays or matrices are built into the Matlab environment. A simple integer is recognised as a matrix of one row and one column. The fundamental structure has a basic data element in a matrix. MATLAB programming platform has numerous advantages over other techniques or languages. In this article, we see how MATLAB is gaining in popularity for deep learning: Why Matlab MATLAB In Deep Learning, Analytics Space Announces R2017B, Massive Update In September.MATLAB Expo 2015 – April, Bangalore & Pune.MathWorks In Collaboration With NVIDIA’s DLI Offers New Deep Learning With MATLAB Course.Comparing Different Programming Languages For Machine Learning.Why You Should Learn Matlab For Data Science.MATLAB provides the ideal environment for deep learning, through model training and deployment. In MATLAB it takes fewer lines of code and builds a machine learning or deep learning model, without needing to be a specialist in the techniques. 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.Deep learning is a technique that is obtaining a foothold beyond multiple disciplines – enabling self-driving cars, predictive fault monitoring of jet engines, and time series forecasting in the economic markets and other use cases. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. 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. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. 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. 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. 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. This book will help you build a foundation in machine learning using MATLAB for beginners. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. * Learn feature selection and extraction for dimensionality reduction leading to improved performance. * Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. * Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. * Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. * 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. What You Will Learn * Learn the introductory concepts of machine learning. A mathematical and statistical background will really help in following this book well. 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. 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.
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