Deep Learning -- A brief Introduction

Gradient descent is a iterative optimization algorithm in Linear Regression to find a local minimum of a differential function. The gradient descent algorithm will choose a random initialization at the beginning for slopes and intercept and calculate the Error term. During every iteration it will back check the error term and back propagate and update the Slope and Intercept values until minimizing the error. Learning rate represents the step size and the Partial derivate represent the direction towards it should move. Always recommended to choose a smaller Learning rate so that it will not miss the local minimum.