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faq/softmax.md

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+# What exactly is the "softmax and the multinomial logistic loss" in the context of machine learning?
+
+The softmax function is simply a generalization of the logistic function that allows us to compute meaningful class-probabilities in multi-class settings (multinomial logistic regression). In softmax, we compute the probability that a particular sample (with net input z) belongs to the *i*th class using a normalization term in the denominator that is the sum of all *M* linear functions:
+
+![](./softmax/softmax_1.png)
+
+In contrast, the logistic function:
+
+![](./softmax/logistic.png)
+
+And for completeness, we define the net input as
+ 
+![](./softmax/net_input.png)
+
+where the weight coefficients of your model are stored as vector "w" and "x" is the feature vector of  your sample.