tweak Zygote page

Author: mcabbott

docs/Project.toml

@@ -7,6 +7,7 @@ MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 Documenter = "0.27"

docs/src/training/zygote.md

@@ -2,12 +2,29 @@
 
 Flux re-exports the `gradient` from [Zygote](https://github.com/FluxML/Zygote.jl), and uses this function within [`train!`](@ref) to differentiate the model. Zygote has its own [documentation](https://fluxml.ai/Zygote.jl/dev/), in particulat listing some [limitations](https://fluxml.ai/Zygote.jl/dev/limitations/).
 
+### Implicit style
+
+Flux uses primarily what Zygote calls "implicit" gradients, [described here](https://fluxml.ai/Zygote.jl/dev/#Explicit-and-Implicit-Parameters-1) in its documentation. 
+
 ```@docs
-Zygote.gradient
-Zygote.jacobian
-Zygote.withgradient
+Zygote.gradient(f, pars::Zygote.Params)
+Zygote.Params
+Zygote.Grads
 ```
 
+### Explicit style
+
+The other way of using Zygote, and using most other AD packages, is to explicitly provide a function and its arguments.
+
+```@docs
+Zygote.gradient(f, args...)
+Zygote.withgradient(f, args...)
+Zygote.jacobian(f, args...)
+```
+
+
+### ChainRules
+
 Sometimes it is necessary to exclude some code, or a whole function, from automatic differentiation. This can be done using [ChainRules](https://github.com/JuliaDiff/ChainRules.jl):
 
 ```@docs