What are the differences between various PyTorch optimizers?

What are the differences between various PyTorch optimizers?

PyTorch offers several optimizers like SGD, Adam, and RMSprop. Each has unique characteristics. Would you like a detailed comparison?

Yes, please explain the key differences between Adam and SGD.

Adam adapts the learning rate for each parameter, making it efficient for complex problems. SGD, on the other hand, uses a fixed learning rate and can be more stable but may require careful tuning.

Which optimizer is generally better for deep learning tasks?

Adam is often preferred for deep learning due to its adaptive learning rates, but it can depend on the specific task and dataset.

Can you provide examples of when to use each optimizer?

Sure! Use SGD for simpler tasks or when you want more control over the learning process. Adam is great for complex models and datasets with noisy gradients.