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Imagine you’re climbing a big hill. You want to reach the top, but you can’t see the whole hill at once. So, what do you do? You take small steps and look around to see if you’re getting closer to the top.
Gradient descent is kind of like that. Instead of a hill, we have a mathematical problem that we want to solve. We want to find the best answer, but we can’t see the whole problem at once. So, we take small steps and keep checking if we’re getting closer to the best answer.
To take these small steps, we use something called a gradient. A gradient tells us which direction to go to get closer to the best answer. It’s like having a compass that points us in the right direction.
Now, let’s say we’re trying to solve a puzzle. We start with a random guess, but it’s probably not the right answer. We check how wrong our guess is by using the gradient. If the gradient tells us to go left, we adjust our guess a little bit to the left. Then, we check again. If the gradient tells us to go right, we adjust our guess a little bit to the right. We keep doing this until we find the best answer.
Just like when you climb a hill, you might take a few wrong steps at first. But each time you check and adjust, you get closer to the top. Gradient descent works the same way. It helps us improve our guess little by little until we find the best answer to the problem we’re trying to solve.
So, gradient descent is like a strategy to help us find the best answer by taking small steps and checking if we’re getting closer. It’s a way to climb the mathematical hills and solve puzzles!
I hope that helps you understand gradient descent, even though it can be a little tricky.