The softmax is a common function in machine learning to map logit values to discrete probabilities. It is often used as the final layer in a neural network applied to multinomial regression problems. Here, we derive its rule for forward-mode AD. Here are the notes: github.com/Ceyron/machine-lea...
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Timestamps:
00:00 What is the softmax?
01:20 Intro
01:35 In general: Jacobian-vector product
02:53 Deriving the Jacobian in index notation
09:07 Evaluating the Jvp in index notation
10:30 The Jvp in symbolic notation
11:40 Full Pushforward rule
12:50 Outro