Gradient descent is an optimization algorithm that is used to find the coefficients of parameters that are used to reduce the cost function to a minimum.
Step 1 : Allocate weights (x,y) with random values and calculate the error (SSE)
Step 2 : Calculate the gradient, i.e., the variation in SSE when the weights (x,y) are changed by a very small value. This helps us move the values of x and y in the direction in which SSE is minimized
Step 3 : Adjust the weights with the gradients to move toward the optimal values where SSE is minimized
Step 4 : Use new weights for prediction and calculating the new SSE
Step 5 : Repeat Steps 2 and 3 until further adjustments to the weights do not significantly reduce the error
