Gradient Descent

Gradient descent (GD) is an iterative method to update the weights of a Linear Regression model to achieve the optimal model.

The downside to GD is that It does not gaurantee the global optimal. To achieve global minimum, tradeoff needs to be made.

Epoch

1 epoch is completed when the entire instances in the training sets are processed once to update the weights of the model.

Hyperparameters

The learning rate is a hyperparameter of gradient descent

LMS

Least-mean-squared is a GD update rule.

Optimizations

There are two optimization of gradient descents:

• Stochastic Gradient Descent
• Batch Gradient Descent