May 29, 2019

LINEAR REGRESSION

1. Minimizing Loss - The goal of a linear regression model is to find the slope and intercept pair that minimizes loss on average across all of the data.

2. Gradient Descent is an iterative algorithm use to tune the parameters in regression models for minimum loss.

3. Learning Rate - the size of the step that gradient descent takes. Finding an adequate value for the learning rate is key to achieve convergence. If this value is too large, the algorithm will never reach the optimus, but if it is too small, it will take too much time to achieve the desired value.

4. How do we know when we should stop changing the parameters m and b? How will we know when our program has learned enough?

5. Convergence is when the loss stops changing ( or changes very slowly) when parameters are changed.

6. The algorithm should converge at the best values for the parameters m and b.