What are advanced optimizers in PyTorch?
An example of an AI chatbot conversation about advanced optimizers in PyTorch.
What are advanced optimizers in PyTorch?
Advanced optimizers in PyTorch include Adam, RMSprop, and AdaGrad, which adapt learning rates for better performance.
How does the Adam optimizer work?
Adam combines the advantages of two other extensions of stochastic gradient descent: AdaGrad and RMSprop. It computes adaptive learning rates for each parameter.
Can you explain the differences between Adam and RMSprop?
Sure! Adam uses both momentum and adaptive learning rates, while RMSprop focuses on adapting the learning rate based on recent gradients.
When should I use each optimizer?
Use Adam for most problems due to its efficiency, but consider RMSprop for recurrent neural networks or when dealing with non-stationary objectives.
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