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.
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https://www.youtube.com/watch?v=jF5nEuePlqE

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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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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.

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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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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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This video has Persian subtitles and noise removal and quality enhancement.
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