Imagine you have a function f(x) that has the minimum point at x=5. To find the minimum point using gradient descent, you can start with an initial guess for x, for example x=4, and then use gradient descent to adjust x until it reaches the optimal value.
Gradient descent is like trying to find the bottom of a big hill.
Imagine you're at the top of a big hill and you want to get to the bottom. You take small steps down the hill, but you don't always go straight down. You look at the ground in front of you to see which way is the steepest down, and then you go that way. You keep doing this, taking small steps and always looking at the ground in front of you, until you finally reach the bottom of the hill.
That's like how gradient descent works!
The role of tangent in gradient descent
In the context of gradient descent, the tangent line helps the algorithm understand which direction it should move in order to find the lowest point on a curve or the most accurate answer.
When the algorithm uses gradient descent, it starts at a point on a curve and then moves in the direction of the tangent line. It then recalculates the tangent line at its new point, and moves in that direction again. The algorithm repeats this process many times, each time moving in the direction of the tangent line, until it reaches the lowest point on the curve.
In summary, the tangent line in gradient descent helps the algorithm understand which direction it should move in order to find the best answer. It is like a guide that helps the algorithm navigate the curve and find the lowest point on the curve efficiently
Gradient descent is an optimization algorithm used in machine learning that helps a model find the best possible solution to a given problem. It starts with an initial guess and then adjusts the parameters of the model until it reaches the lowest point of a function.