What are the differences between optimizers and loss functions in PyTorch?

What are the differences between optimizers and loss functions in PyTorch?

In PyTorch, optimizers are algorithms that adjust the model's parameters to minimize the loss function, which measures how well the model performs.

Can you give examples of common optimizers and loss functions used in PyTorch?

Sure! Common optimizers include SGD, Adam, and RMSprop. For loss functions, you might use CrossEntropyLoss for classification or MSELoss for regression.

How do I choose the right optimizer and loss function for my model?

Choosing the right optimizer depends on your model and data. Adam is often a good starting point, while the loss function should align with your task type.

Are there any best practices for using optimizers and loss functions together?

Yes! It's important to monitor your loss during training and adjust your learning rate or switch optimizers if the loss isn't decreasing.