Скачать или смотреть Optimization of Loss Function and Best Fit Line using Gradient Descent

The presentation explains how Gradient Descent is used to optimize the loss function and find the best-fit line in linear regression.
The loss function measures model error — commonly using Mean Squared Error (MSE).
Gradient Descent minimizes this error by updating parameters step-by-step in the direction of the steepest decrease in loss.
It’s like walking downhill to reach the lowest point, known as the global minimum.
There are three types — Batch, Stochastic, and Mini-batch — each balancing speed and accuracy differently.
Choosing the right learning rate and preventing overfitting are key challenges.
Overall, Gradient Descent is a core algorithm that helps machine learning models learn and improve accuracy efficiently.