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example_notes/Math block test (softmax math function).md

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+# Softmax
+The softmax function transforms a vector into a probability distribution such that the sum of the vector is equal to 1.
+
+$$
+
+$$
+
+## Numerical stability improvements
+
+### Rescaling exponent
+
+Due to the exponent operation, it is very likely that you get $\infty$ values.
+You can prevent this by ensuring that the largest possible exponent is $0$.
+This is typically implement by finding the maximum value and then subtracting it:
+
+```python
+def softmax(x, dim=-1):  
+    c = x.max()  
+    return torch.exp(x - c) / torch.exp(x - c).sum(dim=dim, keepdim=True)
+```
+
+## Calculating the log of the softmax
+
+Calculating the log of the softmax can be numerically unstable so it is better to use the log-softmax approach.