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

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In summary, the conversation is about someone asking for an explanation of the method of Gradient Descent and its use in finding a minimum point in a function. An analogy of a skier on a mountain is used to explain the iterative nature of the solution process. It is suggested to search for more information on Google for a better understanding.
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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.
 

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

What is gradient descent?

Gradient descent is an optimization algorithm used to find the minimum of a function by iteratively adjusting the parameters in the direction of the steepest descent of the function's gradient.

Why is gradient descent important?

Gradient descent is important because it is the backbone of many machine learning algorithms, allowing us to optimize the parameters of a model and make accurate predictions.

How does gradient descent work?

Gradient descent works by starting at a random point and iteratively updating the parameters in the direction of the negative gradient until it reaches a local minimum. This process is repeated until the algorithm converges.

What are the different types of gradient descent?

The two main types of gradient descent are batch gradient descent, which updates the parameters after evaluating the entire dataset, and stochastic gradient descent, which updates the parameters after evaluating a randomly selected subset of the dataset. Other variations include mini-batch gradient descent and online gradient descent.

What are some challenges of using gradient descent?

Some challenges of using gradient descent include choosing an appropriate learning rate, dealing with saddle points and local minima, and avoiding overfitting. It may also require a large amount of data and computational resources to converge to a good solution.

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