What are the different types of optimizers in PyTorch?
An example of an AI chatbot conversation about PyTorch optimizers and their usage.
What are the different types of optimizers in PyTorch?
In PyTorch, common optimizers include SGD, Adam, RMSprop, and Adagrad. Each has unique properties.
Can you explain how the Adam optimizer works?
Sure! Adam combines the advantages of two other extensions of stochastic gradient descent. It keeps an exponentially decaying average of past gradients and squared gradients.
When should I use SGD over Adam?
SGD is often preferred for its simplicity and can generalize better in some cases, especially with large datasets.
Are there any specific parameters I should tune for these optimizers?
Yes, for Adam, you might tune the learning rate, beta1, and beta2. For SGD, the learning rate and momentum are key.
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