4.5. Concise Implementation of Softmax Regression — Dive into Deep Learning 1.0.3 documentation
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larger than the largest number we can have for certain data types every argument is a very large negative number, we will get underflow pass the logits and compute the softmax and its log all at once backpropagation umerical underflow and overflow in the exponentiation.
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- larger than the largest number we can have for certain data types
- every argument is a very large negative number, we will get underflow
- pass the logits and compute the softmax and its log all at once
- backpropagation
- umerical underflow and overflow in the exponentiation.
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