Gradient Descent

In the last lesson, we minimized a loss function to find the best model to predict the tip paid by customers. But there was a drawback with the approach. We manually entered the values of the model parameter $\theta$ and compared the losses. But this approach of manually choosing the values of the model parameters is not scalable because:

• It works only on predetermined values of $\theta$

• Most models have many model parameters and complex structures of the prediction function, for which it will require a lot of time to choose parameters manually.

• We may not choose the best set of model parameters, and then we will not get to the best model.

We need some approach that chooses the model parameters automatically and then arrives at the best model.