Difference between softmax and Logistic Regression? So I read about softmax from this article. Apparently to me these 2 are almost similar, except that the probability of all classes in softmax add to 1. According to their last paragraph for number of classes = 2, softmax reduces to LR. What I want to know is other than number of classes = 2 what are the essential differences between LR and softmax. Like in terms of:


Performance.
Computational Requirements.
Ease of calculation of derivatives.
Ease of visualization.
Number of minimas in the convex cost function, etc.


Other differences are also welcome!

EDIT : I am asking for relative comparisons only, so that at the time of implementation I have no difficulty in selecting which method of implementation to use.
As written, SoftMax is a generalization of Logistic Regression.
Hence:


Performance
If the model has more than 2 classes then you can't compare. Given K = 2 they are the same.
Computation Requirements
Please explain as the computational requirements require the data, enough memory to hold it and enough time to let run.
Ease of Calculation of Derivatives
The cost function is summation hence once you do it for one element you do it fol all.
Ease of Visualization
Well, it is easy to visualize the Confusion Matrix even for K = 10 classes. So no issue here.
Cost Function
The cost function is convex. Yet not Strictly Convex hence there infinite number of minimas.