Move usage and define optimiser sections into docs

Author: logankilpatrick

README.md

@@ -23,54 +23,6 @@ The API for defining an optimiser, and using it is simple.
 ]add Optimisers
 ```
 
-## Define an Optimiser
-
-```julia
-# Define a container to hold any optimiser specific parameters (if any)
-struct Descent{T}
-  η::T
-end
-
-# Define an `apply` rule with which to update the current params
-# using the gradients
-function Optimisers.apply(o::Descent, state, m, m̄)
-    o.η .* m̄, state
-end
-
-Optimisers.init(o, x::AbstractArray) = nothing
-```
-
-Notice that the state is handled separately from the optimiser itself. This
-is a key design principle and allows users to manage their own state explicitly.
-
-It of course also makes it easier to store the state.
-
 ## Usage
 
-```julia
-
-using Flux, Metalhead, Optimisers
-
-o = Optimisers.ADAM() # define an ADAM optimiser with default settings
-st = Optimisers.state(o, m)  # initialize the optimiser before using it
-
-model = ResNet() # define a model to train on
-ip = rand(Float32, 224, 224, 3, 1) # dummy data
-
-m̄, _ = gradient(model, ip) do m, x # calculate the gradients
-  sum(m(x))
-end
-
-
-st, mnew = Optimisers.update(o, st, m, m̄)
-
-# or
-
-st, mnew = o(m, m̄, st)
-```
-
-Notice that a completely new instance of the model is returned. Internally, this
-is handled by [Functors.jl](https://fluxml.ai/Functors.jl), where we do a walk over the
-tree formed by the model and update the parameters using the gradients. Optimisers can
-work with different forms of gradients, but most likely use case are the gradients as
-returned by [Zygote.jl](https://fluxml.ai/Zygote.jl).
+Find out more about using Optimisers.jl [in the docs](https://fluxml.ai/Optimisers.jl/dev/).