Yota 0.8.2, etc

Project.toml

@@ -13,7 +13,7 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 [compat]
 ChainRulesCore = "1"
 Functors = "0.3"
-Yota = "0.8.1"
+Yota = "0.8.2"
 Zygote = "0.6.40"
 julia = "1.6"
 

docs/src/index.md

@@ -62,8 +62,6 @@ tree formed by the model and update the parameters using the gradients.
 
 There is also [`Optimisers.update!`](@ref) which similarly returns a new model and new state,
 but is free to mutate arrays within the old one for efficiency.
-The method of `apply!` for each rule is likewise free to mutate arrays within its state;
-they are defensively copied when this rule is used with `update`.
 (The method of `apply!` above is likewise free to mutate arrays within its state;
 they are defensively copied when this rule is used with `update`.)
 For `Adam()`, there are two momenta per parameter, thus `state` is about twice the size of `model`:
@@ -87,17 +85,18 @@ Yota is another modern automatic differentiation package, an alternative to Zygo
 
 Its main function is `Yota.grad`, which returns the loss as well as the gradient (like `Zygote.withgradient`)
 but also returns a gradient component for the loss function.
-To extract what Optimisers.jl needs, you can write `_, (_, ∇model) = Yota.grad(f, model, data)`
-or, for the Flux model above:
+To extract what Optimisers.jl needs, you can write (for the Flux model above):
 
 ```julia
 using Yota
 
 loss, (∇function, ∇model, ∇image) = Yota.grad(model, image) do m, x
-  sum(m(x))
+  sum(m(x)
 end;
-```
 
+# Or else, this may save computing ∇image:
+loss, (_, ∇model) = grad(m -> sum(m(image)), model);
+```
 
 ## Usage with [Lux.jl](https://github.com/avik-pal/Lux.jl)