The video tutorial shows how to use Python and the OpenAI API to generate images from a chat. The steps include installing Python, choosing a coding environment, installing required libraries using pip, creating an API key by registering on the OpenAI website, and writing Python code in Visual Studio Code. The tutorial demonstrates generating different types of images using the API, specifying image types, and improving image quality. It is noted that the results may vary for the free version of the API.
Complete Version:
https://www.youtube.com/watch?v=jF5nEuePlqE

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#Python #OpenAI #API #imagegeneration #visualstudiocode #Pillow #requests #chatbot #imageprocessing #computergraphics #artificialintelligence #machinelearning #tutorial #imagequality #imageoutput #programming #Python #API #OpenAI #image_generation #Visual_Studio_Code #Pillow #requests #programming #AI #machine_learning #computer_vision #deep_learning #natural_language_processing #chatbot #image_quality #tutorial

In this video, we demonstrate how to use MATLAB and MQL4 programming languages to forecast the price of gold in the forex market. We'll walk you through the process of time series analysis, which involves analyzing and modeling patterns in historical price data to make predictions about future trends.
https://www.youtube.com/watch?v=7zSKoqd1LXs

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✅"How Not To Destroy the World With AI"

Stuart Russell, Smith-Zadeh Chair in Engineering

Wednesday, April 5, 12:00 pm PDT

A Distinguished Lecture on the Status and Future of AI. Presented by CITRIS and the Banatao Institute and BAIR.
This event is open to the public, though in-person seating is currently sold out.

Please find the live-stream link here:

https://www.youtube.com/user/citrisuc/live

The CITRIS Research Exchange and Berkeley Artificial Intelligence Research Lab (BAIR) present a distinguished speaker series exploring the recent breakthroughs of AI, its broader societal implications and its future potential. All talks are free and open to the public.

https://www.berkeley.edu/ai/

❇️more in coment❇️

In this MATLAB programming example, we solve an optimal control problem using the Pontryagin's Maximum Principle. We use the state equations, cost function, Hamiltonian, and costate equations to obtain the optimal control. The solution is obtained using the "dsolve" function, and the results are visualized using MATLAB plots. This example is taken from the "Crack Optimal Control" book.

Watch the video on YouTube:
https://www.youtube.com/watch?v=eN9qZ-dOskM

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John Koza, a Stanford University researcher, developed genetic programming as a method to evolve computer programs by simulating the natural selection process. In this approach, a population of computer programs, composed of primitive functions and terminals, is evolved to solve a given problem. Each program's fitness is determined by its effectiveness in solving the problem. A few programs with high fitness are selected for reproduction, while many participate in a recombination operation called crossover. By iterating this process over multiple generations, the structure of a computer program that effectively solves the problem can emerge.
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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #Part_1

genetic programming, a method for computers to solve problems without explicit programming. Breeding randomly generated programs of different sizes and shapes, the fittest ones are selected for further breeding, creating better solutions over many generations. Stanford professor John Koza's research focuses on exploiting regularities and symmetries of complex environments for hierarchical organization and reuse. The ultimate goal is to enable computers to learn to solve non-trivial problems.
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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #geneticprogramming #AI #computerscience #Part_2

John Koza from Stanford University discuss genetic programming, which automatically creates programs from problem statements. Results produced are competitive with human-produced ones and even infringe on previously patented inventions. Genetic programming is an extension of the genetic algorithm and starts with randomly generated programs that undergo fitness evaluation, selection, and genetic operations. The resulting programs solve a variety of problems, reuse steps, and produce non-trivial results.
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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #geneticprogramming #AI #computerscience #Part_3

Discover the power of genetic programming in creating automated solutions for various problems such as controllers, antennas, genetic networks, and analog electrical circuits. The Genetic Programming IV book and video show how this approach can deliver high-return, human-competitive machine intelligence, and even create patentable inventions. With increasing computer time, results have progressively improved over 15 years. The video highlights the creation of a PID controller using genetic programming, emphasizing that results are human-competitive if they meet specific arm's length criteria.
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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #geneticprogramming #AI #computerscience #Part_4

Comparative Analysis of Bandit Algorithms for Optimal Decision-Making

Explore a comprehensive comparative analysis of various bandit algorithms used in reinforcement learning for optimal decision-making. This video showcases the implementation and evaluation of different methods such as Greedy, Epsilon-Greedy, UCB, and more, highlighting their strengths and performance in selecting optimal actions. Gain insights into the trade-off between exploration and exploitation strategies and learn how these algorithms can enhance decision-making systems. Join us for a deep dive into the world of bandit algorithms and their applications.

YouTube: https://youtu.be/K2dPVza-pSQ


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#ReinforcementLearning #BanditAlgorithms #DecisionMaking #ExplorationVsExploitation #OptimalActionSelection #MachineLearning #DataScience #AI #CodeImplementation #AlgorithmComparison #PerformanceAnalysis

Genetic Algorithm Optimization in MATLAB: Visualizing Fitness Progression

In this video, we showcase the implementation of a Genetic Algorithm (GA) optimization technique using MATLAB. The GA is applied to optimize a 2-variable function by iteratively evolving a population of candidate solutions. The video demonstrates the fitness progression over generations, with the best, worst, and average fitness values plotted. The algorithm incorporates selection, crossover, and mutation operations to drive the evolution of the population. The function landscape, population, and elite individuals are visualized using contour plots and scatter points. Watch this video to gain insights into how a GA can be utilized for optimization tasks and witness the evolution of the population towards finding optimal solutions.

YouTube: https://youtu.be/SJ1zXyEbl0M

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#GeneticAlgorithm #Optimization #MATLAB #FitnessProgression #EvolutionaryAlgorithms #AlgorithmVisualization

MATLAB online

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Reinforcement Learning in Gridworld: Solving the Windy Grid Problem

Watch this video showcasing the implementation of a reinforcement learning algorithm in solving the Windy Grid Problem. The algorithm uses Q-learning with epsilon-greedy exploration to navigate a gridworld with varying wind powers. Learn how the agent learns to reach the goal by optimizing its actions based on rewards and Q-values. The video includes visualizations of the grid, wind powers, and the agent's path.

YouTube: https://youtu.be/AiI_4flFmYc

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#ReinforcementLearning #Qlearning #Gridworld #WindyGridProblem #ArtificialIntelligence #MachineLearning #CodingTutorial #Python #Algorithm #AI

❇️Comprehensive Guide to Multivariable Control: From Differential Equations to QFT Controllers
This tutorial offers an in-depth look at multivariable control systems, particularly within electric arc welding, covering from basic principles like differential equations and block diagrams to advanced topics such as system dynamics, controllability, and advanced control strategies like H-infinity and LQR controllers. It emphasizes the Quantum Field Theory (QFT) controller's role in effectively managing complex control challenges. Designed for students, educators, and engineers, the video bridges theoretical concepts with practical applications, making it a key educational tool in control engineering.
🔻YouTube: https://youtu.be/uB9cJTalCuA
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#MultivariableControlSystems #ElectricArcWeldingControl #DifferentialEquations #StateSpaceModeling #HinfinityController #PIDTuning #LQRController #QFTController

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#clustering
#Ieee_Transactions
#Fuzzy
#Neural_Networks
#GA
#Coming_Soon

% Doc This Code
%% Part 1
% Differential Equations Line 46
% Block diagram Line 76
% State space equations Line 110
% transformation function matrix Line 125
% Denoscription of matrix fraction Line 130

%% Part 2
% System_Pole Line 156
% SmithForm_G Line 162
% MacMilan_pole Line 168
% Zero_Element Line 172
% Zero_transfer Line 181
% Zero_decoupling Line 185
% Controllability and Observability Line 199
% Norm_2 , Norm_infinitely , Norm_Henkel Line 210
% Realization of system balance Line 231
% Reduction of Order Line 238
% Igenvalues of Frobenius Line 262
% Grishorian bands Line 288
% Nyquist Plot Line 303
% Gain-Space Diagram Line 321

%% Part 3
% H-infinity controller Line 357
% PI with pidtune Line 425
% PI 2 (sigma) Line 477
% LQR Controler Line 553
% Optimal LQR with H inf Line 620
% QFT Controler Line 684
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#Code #MIMO

نکاتی در مورد تحلیل آماری و بهینه سازی کد

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❇️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

❇️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

Reinforcement Learning Toolbox™
User's Guide 2023a

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#Reinforcement_Learning #Toolbox #RL #2023a #Environments #Designer #Simulink #Agents #Q_Learning #SARSA #Deep_Q_Network #Policy #Actor #Deep_Deterministic_Policy_Gradient #TD3 #PPO #TRPO #MBPO #NN

نکاتی در مورد متلب 2023a
دارک مود ، نحوه دانلود و نصب ، برخی مزایا و معایب
نحوه دانلود و نصب کتابخانه و مثال های اماده از داخل متلب
نحوه استفاده از راهنما و مثال های آماده
اجرای مثال اماده یادگیری تقویتی پارک اتوماتیک
اجرای مثالی از طراحی اپ
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❇️Track Multiple Vehicles Using a Camera❇️
This example shows how to detect and track multiple vehicles with a monocular camera mounted in a vehicle.

✅Overview
Automated Driving Toolbox™ provides pretrained vehicle detectors and a multi-object tracker to facilitate tracking vehicles around the ego vehicle. The vehicle detectors are based on ACF features and Faster R-CNN, a deep-learning-based object detection technique. The detectors can be easily interchanged to see their effect on vehicle tracking.

The tracking workflow consists of the following steps:

Define camera intrinsics and camera mounting position.

Load and configure a pretrained vehicle detector.

Set up a multi-object tracker.

Run the detector for each video frame.

Update the tracker with detection results.

Display the tracking results in a video.
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#Track #Vehicles #Camera #detector #intrinsics #Driving_Toolbox #R_CNN #deep_learning #ACF