Resnet18

Recall that a Neural Network follows the following construction:

1. Pass data (forward) through model to get predicted values
2. Calculate loss with predicted values against labels
3. Perform backpropagation w.r.t each weight / bias to get the direction in which to move that weight such that it moves closer to the global minima
4. Update parameters with gradients using an optimiser.

ball's pace slows down this makes total fkn sense! if the gradient signs are the same, increasing your confidence in that direction and move further. you want to take less steps over all