1.
Initialize a model with random parameters (weights & biases)
2.
Pass some inputs into the model to obtain predictions
3.
Compare the model’s predictions with the actual target using the loss function
4.
Use an optimization technique to reduce the loss by adjusting the weights & biases of the model
5.
Repeat steps 1 to 4 till the predictions from the model are good enough
