Machine Learning, Deep Learning, Software Development, Robotics, Linear Regression.
always points in the direction of the steepest increase in the objective function. J(θ0, θ1) is the cost function, and θ0 and θ1 are the variable or parameters. The idea here is to find the optimal value for these parameters to minimise the cost function is called gradient descent algorithm.