by Michele Laurelli
The process of adjusting model parameters to minimize the loss function and improve performance.
Optimization uses algorithms like gradient descent, Adam, and RMSprop to update model weights. The goal is to find optimal parameters that minimize prediction error.
Adam optimizer for fast training
SGD with momentum
Learning rate scheduling