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Flux.jl
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The sample gradient should not use the softdash 

While softdash is a very natural and mathematical way of representation, it can be very easily confused with the apostrophe used for LinAlg adjoint. Not worth and unnecessary confusion in a first example of the code.
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Sambit Kumar Dash 2018-09-11 18:58:07 +05:30 committed by GitHub
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8
docs/src/models/basics.md
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@ -10,14 +10,14 @@ using Flux.Tracker
f(x) = 3x^2 + 2x + 1
# df/dx = 6x + 2
f′(x) = Tracker.gradient(f, x)[1]
df(x) = Tracker.gradient(f, x)[1]
f′(2) # 14.0 (tracked)
df(2) # 14.0 (tracked)
# d²f/dx² = 6
f′′(x) = Tracker.gradient(f′, x)[1]
d2f(x) = Tracker.gradient(df, x)[1]
f′′(2) # 6.0 (tracked)
d2f(2) # 6.0 (tracked)
```
(We'll learn more about why these numbers show up as `(tracked)` below.)