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نکاتی در مورد تحلیل آماری و بهینه سازی کد 🆔 @MATLAB_House @MATLABHOUSE

5:51

❇️Fast Self-Supervised Clustering With Anchor Graph
This tutorial showcases the Fast Self-Supervised Clustering method for large-scale, high-dimensional data analysis without labeled samples, using MATLAB. It introduces the Fast Self-Supervised Framework (FSSF) and Balanced K-Means-based Hierarchical K-Means (BKHK) with bipartite graph theory. The method involves four key steps: acquiring an anchor set with BKHK, constructing a bipartite graph, solving the problem using FSSF, and selecting representative points for label propagation. Demonstrated to surpass other methods in performance and efficiency, it offers key insights for those in machine learning and data science.
🔻YouTube: https://youtu.be/_HgnVNGY5gQ
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#MachineLearning #MATLABSimulation #SelfSupervisedClustering #AnchorGraph #IEEE #DataScience #ClusteringAlgorithms #UnsupervisedLearning #BigData #AIResearch

1.32K viewsNaser Pakar, edited 07:21

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4:13

❇️General Fuzzy C-Means Clustering with Objective Function Control
In this MATLAB tutorial, we explore the General Fuzzy C-Means (GFCM) clustering strategy, a novel approach from the IEEE Transactions on Fuzzy Systems that enhances the traditional fuzzy C-means clustering by using an objective function to control fuzziness. This method improves clustering precision by providing a clear definition of fuzzy degree, enabling exact control over results. We demonstrate the GFCM algorithm's adaptability across various distance metrics and fuzzy degrees, emphasizing the importance of choosing the right fuzzy degree. The tutorial covers theoretical basics, practical applications, and the algorithm’s convergence and stability, offering valuable insights for students, researchers, and professionals in data science and machine learning.
🔻YouTube: https://youtu.be/o9DxlIYMNM0
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#FuzzyClustering #DataScience #MachineLearning #IEEE #FuzzySystems #Clustering #ObjectiveFunction #GFCM

621 viewsNaser Pakar, edited 12:53

1:03:36

⚜️Neural network course session one::
1️⃣Introduction to Neural Networks

🔵This video provides an introduction to the fascinating world of neural networks. We explore the biological inspiration behind artificial neural networks, drawing parallels between the human brain and these computational models. Key topics covered include:

✅History of neural networks and major milestones
✅Comparison of biological and artificial neuron speeds
✅Loss of neurons with age and neuroplasticity
✅How the brain processes information and learns
✅Applications of neural networks across diverse fields
✅Further reading resources on neural network fundamentals
To see the next meeting earlier, visit the YouTube
🔻YouTube: second session
https://youtu.be/JtBebQ2CJKs

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#NeuralNetworks #ArtificialIntelligence #MachineLearning #Neurons #BrainInspired #Neuroplasticity #DeepLearning #AI #AINeuralNetworks #ComputationalNeuroscience #NeuralNetworkApplications

1.41K viewsNaser Pakar, edited 08:50

9:36

🔺Fuzzy System Optimization Step-by-Step: Enhancing Interpolation with Genetic Algorithms🔻:

✳️In this detailed tutorial, we dive into the complex world of fuzzy system optimization using a genetic algorithm. Watch as we methodically enhance a fuzzy model to predict outputs more accurately through successive generations of optimization. Starting with the basics, the video explains the generation of membership functions for inputs and outputs and how they evolve through various iterations to minimize error.

🔻YouTube: https://youtu.be/KXM-diXXEyE
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#FuzzyLogic #GeneticAlgorithm #SystemOptimization #MachineLearning #ArtificialIntelligence #DataScience #EngineeringTutorials #MATLAB #OptimizationTechniques #AlgorithmDevelopment #MATLAB_2024

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2.1K viewsNaser Pakar, edited 07:40

3:23:22

⚜️Neural network course session three::
3️⃣An Illustrative Example

🔵In this MATLAB tutorial, learn how to implement Principal Component Analysis (PCA) and Anchor Graphs for dimensionality reduction. The video covers the core concepts, provides step-by-step code explanations, and demonstrates how to visualize and compare results. By the end of this tutorial, you'll be able to apply PCA and Anchor Graphs to your own datasets in MATLAB. Suitable for both beginners and experienced users.

✅Visualizing PCA results in MATLAB
✅Introduction to Anchor Graphs and their advantages
✅Constructing Anchor Graphs in MATLAB
✅Using Anchor Graphs for efficient dimensionality reduction
✅Comparing PCA and Anchor Graph results
🔻YouTube: third session

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#MATLAB #PCA #PrincipalComponentAnalysis #AnchorGraphs #DimensionalityReduction #MachineLearning #DataScience #Tutorial #Eigenvectors #Covariance #DataVisualization #Code #Programming

637 viewsNaser Pakar, edited 12:53

2:42:20

⚜️Neural network course session four::
4️⃣Perceptron Learning Rule

🔵In this MATLAB tutorial video, we dive into the fundamentals of the Perceptron Learning Rule, a powerful algorithm for training single-layer neural networks. Through practical examples and step-by-step explanations, you'll learn how to implement the Perceptron Learning Rule in MATLAB to solve linearly separable classification problems.
We cover key concepts such as:

✅Perceptron architecture and decision boundaries
✅Supervised learning and training sets
✅Weight and bias updates using the Perceptron Learning Rule
✅Convergence and limitations of the Perceptron network
🔻YouTube: third session

Download file and codes (in comment)::
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#MATLAB #MachineLearning #NeuralNetworks #PerceptronLearningRule #AI #ArtificialIntelligence #DeepLearning #DataScience #Programming #Tutorial

731 viewsNaser Pakar, edited 13:33

1:32:59

✳️Deep Belief Network Controller: A Modern Alternative to PID in Simulink

🔰Discover how to replace traditional PID controllers with advanced Deep Belief Network (DBN) controllers in Simulink. This tutorial demonstrates the step-by-step process of implementing a DBN controller, showcasing its advantages over PID in complex control systems. Learn how this cutting-edge AI technique can enhance system performance and adaptability across various engineering applications. Whether you're a control systems engineer, an AI enthusiast, or a student exploring advanced control methods, this video offers valuable insights into the future of intelligent control systems."
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#DeepBeliefNetwork #ControlSystems #Simulink #MachineLearning #PIDController #AIControl #EngineeringTutorial #AdvancedControl #MATLAB #IntelligentSystems

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751 viewsNaser Pakar, edited 07:53

1:22:48

✳️ Deep Network Designer in MATLAB - Quick Guide

🔰 In this tutorial, you’ll learn how to use MATLAB's Deep Network Designer to build and train deep neural networks effortlessly. Whether you're a beginner or advanced user, this step-by-step guide will help you design custom networks, import pre-trained models, adjust layers and hyperparameters, and train/evaluate your models with ease.

Produced by Saeed Heibati and Amirhossein Jalali, with consulting by Naser Pakar.

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#DeepLearning #MATLAB #NeuralNetworks #TransferLearning #AI #MachineLearning #DLInMATLAB #DeepNetworkTutorial

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1.39K viewsNaser Pakar, edited 09:00

17:15