reduce the amount of text on welcome page

Author: mcabbott

docs/src/index.md

@@ -2,28 +2,26 @@
 
 Flux is a library for machine learning. It comes "batteries-included" with many useful tools built in, but also lets you use the full power of the Julia language where you need it. We follow a few key principles:
 
-* **Doing the obvious thing**. Flux has relatively few explicit APIs for features like regularisation or embeddings. Instead, writing down the mathematical form will work – and be fast.
-* **Extensible by default**. Flux is written to be highly extensible and flexible while being performant. Extending Flux is as simple as using your own code as part of the model you want - it is all [high-level Julia code](https://github.com/FluxML/Flux.jl/blob/ec16a2c77dbf6ab8b92b0eecd11661be7a62feef/src/layers/recurrent.jl#L131). When in doubt, it’s well worth looking at [the source](https://github.com/FluxML/Flux.jl/tree/master/src). If you need something different, you can easily roll your own.
-* **Play nicely with others**. Flux works well with Julia libraries from [images](https://github.com/JuliaImages/Images.jl) to [differential equation solvers](https://github.com/SciML/DifferentialEquations.jl), so you can easily build complex data processing pipelines that integrate Flux models.
+* **Doing the obvious thing**. Flux has relatively few explicit APIs. Instead, writing down the mathematical form will work – and be fast.
+* **Extensible by default**. Flux is written to be highly flexible while being performant. Extending Flux is as simple as using your own code as part of the model you want - it is all [high-level Julia code](https://github.com/FluxML/Flux.jl/tree/master/src).
+* **Play nicely with others**. Flux works well with unrelated Julia libraries from [images](https://github.com/JuliaImages/Images.jl) to [differential equation solvers](https://github.com/SciML/DifferentialEquations.jl), rather than duplicating them.
 
 ## Installation
 
 Download [Julia 1.6](https://julialang.org/downloads/) or later, preferably the current stable release. You can add Flux using Julia's package manager, by typing `] add Flux` in the Julia prompt.
 
 This will automatically install several other packages, including [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) which supports Nvidia GPUs. To directly access some of its functionality, you may want to add `] add CUDA` too. The page on [GPU support](gpu.md) has more details.
 
-Other closely associated packages, also installed automatically, include [Zygote](https://github.com/FluxML/Zygote.jl), [Optimisers](https://github.com/FluxML/Optimisers.jl), [NNlib](https://github.com/FluxML/NNlib.jl), [Functors](https://github.com/FluxML/Functors.jl) and [MLUtils](https://github.com/JuliaML/MLUtils.jl).
-
 ## Learning Flux
 
-The [quick start](@ref man-quickstart) page trains a simple neural network.
+The **[quick start](@ref man-quickstart)** page trains a simple neural network.
 
 This rest of the **guide** provides a from-scratch introduction to Flux's take on models and how they work, starting with [fitting a line](@ref man-overview). Once you understand these docs, congratulations, you also understand [Flux's source code](https://github.com/FluxML/Flux.jl), which is intended to be concise, legible and a good reference for more advanced concepts.
 
-The **reference** section contains API listings. The same text is avalable at the Julia prompt, by typing for example `?gpu`.
-
 There are some **tutorials** about building particular models. The **[model zoo](https://github.com/FluxML/model-zoo/)** has starting points for many other common ones. And finally, the **[ecosystem page](ecosystem.md)** lists packages which define Flux models.
 
+The **reference** section contains API listings, including some companion packages: [Zygote](https://github.com/FluxML/Zygote.jl) (automatic differentiation), [Optimisers](https://github.com/FluxML/Optimisers.jl) (training), [NNlib](https://github.com/FluxML/NNlib.jl) (misc functions) and more.
+
 ## Community
 
 Everyone is welcome to join our community on the [Julia discourse forum](https://discourse.julialang.org/), or the [slack chat](https://discourse.julialang.org/t/announcing-a-julia-slack/4866) (channel #machine-learning). If you have questions or issues we'll try to help you out.