Articles by "MATLAB"

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Kamal I. M. Al-Malah ... 592 pages - Language: ‎English - Publisher: ‎Wiley; (October, 2023).

Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code. The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues.

Readers will also find information on: Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning) + Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response) + Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing steps + Retraining and creation for image labeling, object identification, regression classification, and text recognition. Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.

Holly Moore ... 705 pages - Language: English - Publisher: Pearson; 6th edition (February, 2022).


MATLAB For Engineers starts at the beginning to introduce first-year engineering students to MATLAB. Starting with basic algebra, you'll learn how MATLAB can be used to solve a wide range of engineering problems. Examples taken from concepts presented in early chemistry, physics, and first- and second-year engineering classes are included. When the text covers new subjects, like statistics and matrix algebra, brief background information is used to support your success. As you work through hands-on examples and exercises, you'll learn to apply a consistent problem-solving methodology to help you reach a solution.

The 6th Edition reflects the MATLAB software release R2021B. Updated screenshots, new data, new problems and discussions offer a current view of the coding language and platform you'll use in your classes and career.

MATLAB R2024b v24.2.0.2712019 x64 [Size: 12.530 GB] ... MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.
 
Typical uses include: Math and computation + Algorithm development + Modeling, simulation, and prototyping + Data analysis, exploration, and visualization + Scientific and engineering graphics + Application development, including + Graphical User Interface building. MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar noninteractive language such as C or Fortran. The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy access to matrix software developed by the LINPACK and EISPACK projects, which together represent the state-of-the-art in software for matrix computation. 

MATLAB has evolved over a period of years with input from many users. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high-productivity research, development, and analysis. MATLAB features a family of application-specific solutions called toolboxes. Very important to most users of MATLAB, toolboxes allow you to learn and apply specialized technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that extend the MATLAB environment to solve particular classes of problems. Areas in which toolboxes are available include signal processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and many others.

Teacher:
Nastaran Reza Nazar Zadeh - Language: English - Videos: 50 - Duration: 4 hours and 11 minutes.

Artificial Neural Network and Machine Learning using MATLAB This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don’t understand machine learning and Artificial Neural Network from the ground up. In this course, we introduce a comprehensive training of multilayer perceptron neural networks or MLP in MATLAB, in which, in addition to reviewing the theories related to MLP neural networks, the practical implementation of this type of network in MATLAB environment is also fully covered. MATLAB offers specialized toolboxes and functions for working with Machine Learning and Artificial Neural Networks which makes it a lot easier and faster for you to develop a NN. At the end of this course, you’ll be able to create a Neural Network for applications such as classification, clustering, pattern recognition, function approximation, control, prediction, and optimization.

What you’ll learn: Develop a multilayer perceptron neural networks or MLP in MATLAB using Toolbox + Apply Artificial Neural Networks in practiceBuilding Artificial Neural Network Model + Knowledge on Fundamentals of Machine Learning and Artificial Neural Network + Understand Optimization methods + Understand the Mathematical Model of a Neural Network + Understand Function approximation methodology + Make powerful analysis + Knowledge on Performance Functions + Knowledge on Training Methods for Machine Learning.

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