Deep Learning
AdamW
Adam with weight decay applied directly to the parameter update rather than added to the gradient.
Why interviewers ask about it
Under plain Adam, L2 in the loss gets divided by √v, so parameters with large gradients receive the least decay - the opposite of intended. Never decay biases or norm parameters.
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