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Tagged with mini-batch-gradient-descent gradient
2 questions
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Why to use gradient accumulation?
I know that gradient accumulation is (1) a way to reduce memory usage while still enabling the machine to fit a large dataset (2) reducing the noise of the gradient compared to SGD, and thus smoothing ...
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Why would one prefer the gradient of the sum rather than the sum of the gradients?
When gradients are aggregated over mini batches, I sometimes see formulations like this, e.g., in the "Deep Learning" book by Goodfellow et al.
$$\mathbf{g} = \frac{1}{m} \nabla_{\mathbf{w}} ...
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