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Tag: MATLAB
Matlab for Engineers
Price: $115.99
(as of Jan 21,2025 21:31:37 UTC – Details)
Publisher : Pearson College Div; 3rd edition (January 1, 2011)
Language : English
Paperback : 656 pages
ISBN-10 : 0132103257
ISBN-13 : 978-0132103251
Item Weight : 2.3 pounds
Dimensions : 8 x 0.75 x 10 inchesCustomers say
Customers find the book easy to use and a good reference for learning programming basics. They appreciate the thorough examples and range of problem topics that cover Matlab’s functionality. The book is described as sturdy and in good condition.
AI-generated from the text of customer reviews
Are you an engineer looking to enhance your skills in data analysis, modeling, and simulation? Look no further than Matlab!Matlab is a powerful software tool that is widely used in the engineering field for its ability to quickly and efficiently analyze and visualize complex data sets. Whether you are working on signal processing, control systems, image processing, or any other engineering discipline, Matlab can help you streamline your workflow and make your projects more efficient.
In this post, we will explore some of the key features of Matlab that make it a valuable tool for engineers. From its extensive library of built-in functions to its user-friendly interface, Matlab offers a wide range of capabilities that can help you tackle even the most challenging engineering problems.
So whether you are a seasoned engineer looking to expand your skill set or a student just starting out in the field, consider adding Matlab to your toolbox. With its versatility and power, Matlab can help you take your engineering projects to the next level.
#Matlab #Engineers,machine learning: an applied mathematics introductionIntroduction to Engineering Programming: In C, Matlab and Java
Price:$199.95– $16.79
(as of Jan 19,2025 18:53:02 UTC – Details)
Publisher : Wiley; 1st edition (March 22, 1999)
Language : English
Paperback : 672 pages
ISBN-10 : 0471001163
ISBN-13 : 978-0471001164
Item Weight : 1.9 pounds
Dimensions : 7.48 x 0.95 x 9.39 inches
Introduction to Engineering Programming: In C, Matlab, and JavaEngineering programming is an essential skill for engineers in various fields, as it allows them to solve complex problems, analyze data, and create simulations. In this post, we will introduce you to three popular programming languages used in engineering: C, Matlab, and Java.
C is a powerful and efficient programming language that is commonly used in engineering for system programming, embedded systems, and high-performance applications. It is a low-level language, meaning that it allows for direct manipulation of hardware and memory, making it ideal for tasks that require precise control over the computer’s resources.
Matlab is a high-level programming language that is widely used in engineering for numerical computation, data analysis, and visualization. It is known for its ease of use and powerful tools for matrix manipulation, signal processing, and image processing. Matlab also has a large library of built-in functions and toolboxes that make it easy to perform complex engineering tasks.
Java is a versatile programming language that is used in engineering for building web applications, mobile apps, and desktop applications. It is known for its platform independence, meaning that Java programs can run on any device that has a Java Virtual Machine (JVM). Java is also popular for its object-oriented programming features, which allow engineers to create reusable and modular code.
In this series, we will explore the basics of programming in C, Matlab, and Java, including data types, control structures, functions, and object-oriented programming concepts. By the end of this series, you will have a solid foundation in engineering programming and be able to apply your skills to a wide range of engineering tasks. Stay tuned for more in-depth tutorials and examples on each of these programming languages!
#Introduction #Engineering #Programming #Matlab #Java,machine learning: an applied mathematics introductionMATLAB: A Practical Introduction to Programming and Problem Solving
Price:$64.95– $22.95
(as of Jan 19,2025 13:21:47 UTC – Details)
Publisher : Butterworth-Heinemann; 5th edition (August 31, 2018)
Language : English
Paperback : 626 pages
ISBN-10 : 0128154799
ISBN-13 : 978-0128154793
Item Weight : 2.31 pounds
Dimensions : 7.5 x 1 x 9.5 inchesCustomers say
Customers find this book provides a good introduction to Matlab programming. They say it takes you from beginner to intermediate level and gives necessary tools to explore more. The pages are comfortable to read, and the author is liked.
AI-generated from the text of customer reviews
MATLAB: A Practical Introduction to Programming and Problem SolvingAre you looking to improve your programming skills and problem-solving abilities? Look no further than MATLAB. MATLAB is a powerful programming language and environment that is widely used in engineering, science, and mathematics.
In this post, we will provide a practical introduction to MATLAB programming and problem-solving techniques. Whether you are a beginner or an experienced programmer, this post will help you develop the skills needed to tackle complex problems and create efficient solutions.
We will cover the basics of MATLAB programming, including variables, data types, arrays, loops, and functions. We will also discuss how to use MATLAB for data visualization, numerical computation, and optimization.
By the end of this post, you will have a solid foundation in MATLAB programming and be able to apply your skills to real-world problems. So, if you are ready to take your programming skills to the next level, let’s dive into the world of MATLAB.
#MATLAB #Practical #Introduction #Programming #Problem #Solving,machine learning: an applied mathematics introduction
Modeling Derivatives Applications in Matlab, C++, And Excel – Hardcover Lond…
Modeling Derivatives Applications in Matlab, C++, And Excel – Hardcover Lond…
Price : 49.50
Ends on : N/A
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Modeling Derivatives Applications in Matlab, C++, And Excel – Hardcover London EditionLooking to expand your knowledge and skills in modeling derivatives applications? Look no further than this comprehensive guide, now available in a limited edition hardcover London edition.
This book covers the fundamentals of modeling derivatives using popular programming languages such as Matlab, C++, and Excel. Whether you are a beginner looking to dive into the world of derivatives or a seasoned professional wanting to enhance your skills, this book has something for everyone.
With step-by-step instructions, practical examples, and real-world applications, you will learn how to effectively model various derivatives instruments, including options, futures, and swaps. You will also gain valuable insights into risk management, pricing models, and hedging strategies.
Don’t miss out on this opportunity to elevate your understanding of derivatives modeling. Order your copy of Modeling Derivatives Applications in Matlab, C++, And Excel – Hardcover London Edition today!
#Modeling #Derivatives #Applications #Matlab #Excel #Hardcover #Lond.., Data Management
A Practical Approach for Image Processing & Computer Vision In MATLAB: A Practical Approach for Image Processing & Computer Vision In MATLAB
Price: $42.73
(as of Dec 28,2024 11:29:35 UTC – Details)
Publisher : CreateSpace Independent Publishing Platform (December 26, 2016)
Language : English
Paperback : 232 pages
ISBN-10 : 1541286022
ISBN-13 : 978-1541286023
Item Weight : 1.68 pounds
Dimensions : 8.5 x 0.55 x 11 inches
Image processing and computer vision are important fields in the realm of artificial intelligence and machine learning. MATLAB, a powerful software tool, provides a comprehensive platform for implementing various image processing and computer vision algorithms.In this post, we will discuss a practical approach for image processing and computer vision in MATLAB. We will cover the basic steps involved in processing and analyzing images using MATLAB, along with some common techniques and algorithms.
1. Loading and displaying images:
The first step in image processing is to load an image into MATLAB using the ‘imread’ function. Once the image is loaded, it can be displayed using the ‘imshow’ function.2. Preprocessing images:
Image preprocessing involves enhancing the quality of the image before further analysis. This can include operations such as noise reduction, image sharpening, and contrast enhancement. MATLAB provides various functions for performing these operations, such as ‘imfilter’ for filtering and ‘imadjust’ for adjusting image contrast.3. Image segmentation:
Image segmentation is the process of dividing an image into different regions or objects. MATLAB offers several segmentation algorithms, such as thresholding, edge detection, and clustering. These algorithms can be used to identify and extract specific features or objects in an image.4. Feature extraction:
Once the image has been segmented, features can be extracted from the regions of interest. These features can include color, texture, shape, and size. MATLAB provides functions for extracting features, such as ‘regionprops’ for computing region properties and ‘extractFeatures’ for extracting features from images.5. Object detection and recognition:
Object detection and recognition are key tasks in computer vision. MATLAB provides tools for implementing object detection algorithms, such as Haar cascades and deep learning models. These algorithms can be used to detect and recognize objects in images or videos.6. Image classification:
Image classification is the process of categorizing images into different classes or categories. MATLAB offers several classification algorithms, such as support vector machines, k-nearest neighbors, and deep learning models. These algorithms can be trained on a labeled dataset to classify images into different classes.In conclusion, MATLAB provides a comprehensive platform for image processing and computer vision. By following a practical approach and utilizing the various tools and algorithms available in MATLAB, you can implement sophisticated image processing and computer vision applications. Whether you are a beginner or an expert in the field, MATLAB offers a versatile and powerful environment for exploring and implementing image processing and computer vision algorithms.
#Practical #Approach #Image #Processing #Computer #Vision #MATLAB #Practical #Approach #Image #Processing #Computer #Vision #MATLAB
Robotic Vision: Fundamental Algorithms in MATLAB® (Springer Tracts in Advanced Robotics Book 142)
Price: $40.55
(as of Dec 28,2024 05:48:15 UTC – Details)
ASIN : B09JLJ2J1X
Publisher : Springer (October 15, 2021)
Publication date : October 15, 2021
Language : English
File size : 84513 KB
Text-to-Speech : Not enabled
Enhanced typesetting : Not Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Format : Print Replica
Are you interested in learning about the fundamental algorithms behind robotic vision? Look no further than “Robotic Vision: Fundamental Algorithms in MATLAB®” (Springer Tracts in Advanced Robotics Book 142). This comprehensive book covers essential concepts in robotic vision, including image processing, feature extraction, object recognition, and more, all using MATLAB® as the programming platform.Whether you’re a student, researcher, or industry professional, this book provides valuable insights into the world of robotic vision and how to implement key algorithms in MATLAB®. With clear explanations, practical examples, and step-by-step guides, you’ll gain a solid understanding of the foundational principles that underpin robotic vision systems.
Don’t miss out on this essential resource for anyone looking to delve into the exciting field of robotic vision. Order your copy of “Robotic Vision: Fundamental Algorithms in MATLAB®” today and take your knowledge to the next level.
#Robotic #Vision #Fundamental #Algorithms #MATLAB #Springer #Tracts #Advanced #Robotics #Book
PREDICTIVE ARTIFICIAL INTELLIGENCE TECHNIQUES: CLASSIFICATION WITH SVM, DISCRIMINANT ANALYSIS AND DECISION TREES. Examples with MATLAB
Price: $29.99
(as of Dec 28,2024 03:11:10 UTC – Details)
Publisher : Scientific Books (July 31, 2024)
Language : English
Paperback : 241 pages
ISBN-10 : 1304153428
ISBN-13 : 978-1304153425
Item Weight : 1.19 pounds
Dimensions : 7 x 0.55 x 10 inchesPredictive artificial intelligence techniques are powerful tools that can be used to classify and predict outcomes based on data. In this post, we will explore three popular techniques – Support Vector Machines (SVM), Discriminant Analysis, and Decision Trees – and how they can be implemented using MATLAB.
Support Vector Machines (SVM) is a supervised learning algorithm that can be used for classification or regression tasks. SVM works by finding the optimal hyperplane that separates data points into different classes. In MATLAB, you can use the
fitcsvmfunction to train an SVM model andpredictfunction to make predictions on new data.% Example of SVM classification using MATLAB<br /> data = load('iris_dataset.mat');<br /> X = data.X;<br /> Y = data.Y;<br /> <br /> SVMModel = fitcsvm(X,Y);<br /> predictions = predict(SVMModel, newX);<br /> ```<br /> <br /> Discriminant Analysis is another popular technique for classification tasks. It works by modeling the probability distribution of each class and then using Bayes' theorem to make predictions. In MATLAB, you can use the `fitcdiscr` function to train a discriminant analysis model and `predict` function to make predictions.<br /> <br /> ```matlab<br /> % Example of Discriminant Analysis classification using MATLAB<br /> data = load('iris_dataset.mat');<br /> X = data.X;<br /> Y = data.Y;<br /> <br /> DAModel = fitcdiscr(X,Y);<br /> predictions = predict(DAModel, newX);<br /> ```<br /> <br /> Decision Trees are a popular machine learning algorithm that uses a tree-like structure to make decisions. Each node in the tree represents a feature and each leaf node represents a class label. In MATLAB, you can use the `fitctree` function to train a decision tree model and `predict` function to make predictions.<br /> <br /> ```matlab<br /> % Example of Decision Trees classification using MATLAB<br /> data = load('iris_dataset.mat');<br /> X = data.X;<br /> Y = data.Y;<br /> <br /> TreeModel = fitctree(X,Y);<br /> predictions = predict(TreeModel, newX);<br /> ```<br /> <br /> In conclusion, predictive artificial intelligence techniques such as SVM, Discriminant Analysis, and Decision Trees can be powerful tools for classification tasks. By using MATLAB, you can easily implement these techniques and make accurate predictions based on your data.#PREDICTIVE #ARTIFICIAL #INTELLIGENCE #TECHNIQUES #CLASSIFICATION #SVM #DISCRIMINANT #ANALYSIS #DECISION #TREES #Examples #MATLAB
MATLAB High Performance Numeric Computation and Visualization Software User’s Gu
MATLAB High Performance Numeric Computation and Visualization Software User’s Gu
Price :12.00– 7.80
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ideAre you ready to take your MATLAB skills to the next level? Look no further than our MATLAB High Performance Numeric Computation and Visualization Software User’s Guide. This comprehensive guide is designed to help users harness the full power of MATLAB for complex numerical computations and stunning visualizations.
Inside this guide, you will find detailed instructions on how to optimize your MATLAB code for high performance, including tips on vectorization, parallel computing, and memory management. You will also learn how to create beautiful plots and animations using MATLAB’s powerful visualization tools.
Whether you are a beginner looking to expand your MATLAB knowledge or an experienced user seeking to enhance your skills, this user’s guide has something for everyone. So don’t wait, unleash the full potential of MATLAB with our High Performance Numeric Computation and Visualization Software User’s Guide today!
#MATLAB #High #Performance #Numeric #Computation #Visualization #Software #Users, high-performance computing
MACHINE LEARNING WITH MATLAB. kNN, CLUSTER ANALYSIS AND PATTERN RECOGNITION WITH NEURAL NETWORKS
Price: $25.99
(as of Dec 27,2024 15:26:03 UTC – Details)
Publisher : Scientific Books (May 23, 2022)
Language : English
Paperback : 209 pages
ISBN-10 : 147169772X
ISBN-13 : 978-1471697722
Item Weight : 1.41 pounds
Dimensions : 8.27 x 0.48 x 11.69 inches
Machine learning is a powerful tool that allows computers to learn from data and make decisions or predictions without being explicitly programmed. MATLAB, a popular programming language and environment for scientific computing, provides a comprehensive set of tools for machine learning. In this post, we will explore some key concepts in machine learning using MATLAB, including k-nearest neighbors (kNN), cluster analysis, and pattern recognition with neural networks.K-Nearest Neighbors (kNN) is a simple and intuitive algorithm for classification and regression tasks. In kNN, the class label or value of a new data point is predicted based on the majority class or average of the k nearest data points in the training set. MATLAB provides efficient functions for implementing kNN, making it easy to build and evaluate kNN models for a wide range of applications.
Cluster analysis is another important technique in machine learning, used to group similar data points into clusters or segments. MATLAB offers a variety of clustering algorithms, such as k-means clustering and hierarchical clustering, that can be used to discover patterns and insights in data sets. By visualizing the clusters and analyzing their characteristics, researchers and practitioners can gain a deeper understanding of complex data sets.
Pattern recognition with neural networks is a powerful approach for solving classification and regression problems, inspired by the structure and function of the human brain. MATLAB includes a comprehensive neural network toolbox that allows users to design, train, and evaluate neural network models for a variety of tasks. By adjusting the architecture, activation functions, and training parameters of neural networks, users can optimize performance and accuracy for specific applications.
In conclusion, MATLAB provides a rich set of tools for machine learning, including kNN, cluster analysis, and pattern recognition with neural networks. By leveraging these tools and techniques, researchers and practitioners can unlock the potential of their data and make informed decisions in a wide range of domains. Whether you are new to machine learning or a seasoned expert, MATLAB offers a flexible and powerful environment for exploring and implementing advanced machine learning algorithms.
#MACHINE #LEARNING #MATLAB #kNN #CLUSTER #ANALYSIS #PATTERN #RECOGNITION #NEURAL #NETWORKS
Data Science Algorithms for Unsupervised Learning: CLUSTER ANALYSIS AND KNN CLASSIFIERS. Examples with MATLAB
Price: $29.50
(as of Dec 27,2024 11:57:13 UTC – Details)
Unsupervised learning is a powerful tool in data science that allows us to discover patterns and relationships within data without the need for labeled outcomes. Cluster analysis and K-nearest neighbors (KNN) classifiers are two popular algorithms used in unsupervised learning. In this post, we will explore these algorithms and provide examples of implementing them in MATLAB.
Cluster analysis is a method of grouping similar data points together based on certain criteria. One common algorithm used for cluster analysis is K-means clustering. K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. Let’s take a look at an example of implementing K-means clustering in MATLAB:
% Generate sample data<br /> data = rand(100, 2);<br /> <br /> % Perform k-means clustering<br /> k = 3; % Number of clusters<br /> [idx, centroids] = kmeans(data, k);<br /> <br /> % Plot the data points and centroids<br /> figure;<br /> gscatter(data(:,1), data(:,2), idx);<br /> hold on;<br /> plot(centroids(:,1), centroids(:,2), 'kx', 'MarkerSize', 15, 'LineWidth', 3);<br /> ```<br /> <br /> K-nearest neighbors (KNN) is a simple and effective algorithm for classification tasks in unsupervised learning. In KNN, the class of a data point is determined by a majority vote of its k nearest neighbors. Let's see how we can implement KNN classification in MATLAB:<br /> <br /> ```matlab<br /> % Generate sample data<br /> data = rand(100, 2);<br /> labels = randi([1, 3], 100, 1); % Random labels for classification<br /> <br /> % Create a KNN classifier<br /> knn = fitcknn(data, labels);<br /> <br /> % Predict the class of a new data point<br /> new_data = [0.5, 0.5];<br /> predicted_class = predict(knn, new_data);<br /> disp(['Predicted class: ' num2str(predicted_class)]);<br /> ```<br /> <br /> In this post, we have explored the concepts of cluster analysis and KNN classifiers in unsupervised learning and provided examples of implementing these algorithms in MATLAB. These algorithms can be powerful tools for uncovering hidden patterns and relationships within data without the need for labeled outcomes.#Data #Science #Algorithms #Unsupervised #Learning #CLUSTER #ANALYSIS #KNN #CLASSIFIERS #Examples #MATLAB
