TastyPancakes
/
Flux.jl
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

docs updates

This commit is contained in:
Mike J Innes 2018-09-05 16:01:57 +01:00
parent ec16a2c77d
commit b7eaf393fc
1 changed files with 9 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
docs/src/index.md
View File

@ -1,18 +1,17 @@
# Flux: The Julia Machine Learning Library
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. The whole stack is implemented in clean Julia code (right down to the [GPU kernels](https://github.com/FluxML/CuArrays.jl)) and any part can be tweaked to your liking.
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.
* **You could have written Flux**. All of it, from [LSTMs](https://github.com/FluxML/Flux.jl/blob/ec16a2c77dbf6ab8b92b0eecd11661be7a62feef/src/layers/recurrent.jl#L131) to [GPU kernels](https://github.com/JuliaGPU/CuArrays.jl), is straightforward Julia code. When it doubt, it’s well worth looking at [the source](https://github.com/FluxML/Flux.jl/). If you need something different, you can easily roll your own.
* **Play nicely with others**. Flux works well with Julia libraries from [data frames](https://github.com/JuliaComputing/JuliaDB.jl) and [images](https://github.com/JuliaImages/Images.jl) to [differential equation solvers](https://github.com/JuliaDiffEq/DifferentialEquations.jl), so you can easily build complex data processing pipelines that integrate Flux models.
# Installation
Install [Julia 0.6.0 or later](https://julialang.org/downloads/), if you haven't already.
Download [Julia 1.0](https://julialang.org/) or later, if you haven't already. You can add Flux from using Julia's package manager, by typing `] add Flux` in the Julia prompt.
```julia
Pkg.add("Flux")
# Optional but recommended
Pkg.update() # Keep your packages up to date
Pkg.test("Flux") # Check things installed correctly
```
If you have CUDA you can also run `] add CuArrays` to get GPU support; see [here](gpu.md) for more details.
Start with the [basics](models/basics.md). The [model zoo](https://github.com/FluxML/model-zoo/) is also a good starting point for many common kinds of models.
# Learning Flux
See [GPU support](gpu.md) for more details on installing and using Flux with GPUs.
There are several different ways to learn Flux. If you just want to get started writing models, the [model zoo](https://github.com/FluxML/model-zoo/) gives good starting points for many common ones. This documentation provides a reference to all of Flux's APIs, as well as a from-scratch introduction to Flux's take on models and how they work. 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.