maybe we should have a much simpler readme example

README.md

@@ -20,24 +20,22 @@ Flux is an elegant approach to machine learning. It's a 100% pure-Julia stack, a
 
 Works best with [Julia 1.8](https://julialang.org/downloads/) or later. Here's a very short example to try it out:
 ```julia
-using Flux  # should install everything for you, including CUDA
+using Flux, Plots
+data = [([x], x-cbrt(x)) for x in range(-2, 2, 100)]
 
-x = hcat(digits.(0:3, base=2, pad=2)...)  # data for the XOR problem
-y = Flux.onehotbatch(xor.(eachrow(x)...), 0:1)
+model = Chain(Dense(1 => 10, tanh), Dense(10 => 1), only)
 
-model = Chain(Dense(2 => 3, sigmoid), BatchNorm(3), Dense(3 => 2))
-pars = Flux.params(model)  # a dictionary of arrays in model
-optim = Adam(0.1, (0.7, 0.95))
-
-for _ in 1:100
-    grad = gradient(() -> Flux.logitcrossentropy(model(x), y), pars)
-    Flux.update!(optim, pars, grad)  # this changes model & optim
+loss(x,y) = abs2(model(x) - y)
+optim = Flux.Adam()
+for epoch in 1:1000
+  Flux.train!(loss, Flux.params(model), data, optim)
 end
 
-all((softmax(model(x)) .> 0.5) .== y)  # usually 100% accuracy.
+plot(x -> x-cbrt(x), -2, 2, legend=false)
+scatter!(-2:0.1:2, [model([x]) for x in -2:0.1:2])
 ```
 
-The [quickstart page](https://fluxml.ai/Flux.jl/stable/models/quickstart/) has a longer version. See the [documentation](https://fluxml.github.io/Flux.jl/) for details, or the [model zoo](https://github.com/FluxML/model-zoo/) for examples. Ask questions on the [Julia discourse](https://discourse.julialang.org/) or [slack](https://discourse.julialang.org/t/announcing-a-julia-slack/4866).
+The [quickstart page](https://fluxml.ai/Flux.jl/stable/models/quickstart/) has a longer example. See the [documentation](https://fluxml.github.io/Flux.jl/) for details, or the [model zoo](https://github.com/FluxML/model-zoo/) for examples. Ask questions on the [Julia discourse](https://discourse.julialang.org/) or [slack](https://discourse.julialang.org/t/announcing-a-julia-slack/4866).
 
 If you use Flux in your research, please [cite](CITATION.bib) our work.