Gradient Descent: What is It & How to Use It?

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SUMMARY

Gradient Descent is an optimization algorithm used to find the minimum point of a function, exemplified by the equation h(x,y) = x^2 + y^2. The method involves iteratively moving in the direction of the steepest descent until a minimum is reached. Each iteration adjusts the current position based on the gradient of the function, akin to a skier navigating downhill to find the lowest point in a valley. This process continues until the minimum is located, demonstrating the iterative nature of Gradient Descent.

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  • Understanding of basic calculus, specifically derivatives
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  • Knowledge of iterative algorithms
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DeanDeanDean
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Hello.

I hope I've chosen the correct place to post this. Apologies if it is not.

Could somebody explain the method of Gradient Descent to me or give me a link to a good explanation? For example, if h(x,y) = x^2 + y^2, what would I do to find a minimum point using gradient descent? I've read things about iterations but what exactly are these iterations?

Thank you.
 
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Welcome to PF!

An analogy often used is a skier on a mountain trying to find the lowest point in the valley. Go downhill in the steepest direction and stop when you hit a minimum point. Change directions to the steepest downhill direction and repeat. That's the iterative nature of the solution process. If the function is a perfect bowl there is only one iteration. You can do a search on Google and find more information.
 

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