Merge pull request #2016 from Saransh-cpp/linear-regression

A new linear regression tutorial

Author: ToucheSir

docs/Project.toml

@@ -1,12 +1,16 @@
 [deps]
 BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
+MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]

docs/make.jl

@@ -1,10 +1,10 @@
-using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore, Statistics
+using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore, Plots, MLDatasets, Statistics, DataFrames
 
 
 DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive = true)
 
 makedocs(
-    modules = [Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore, Base, Statistics],
+    modules = [Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore, Base, Plots, MLDatasets, Statistics, DataFrames],
     doctest = false,
     sitename = "Flux",
     # strict = [:cross_references,],
@@ -41,11 +41,12 @@ makedocs(
              "Flat vs. Nested 📚" => "destructure.md",
              "Functors.jl 📚 (`fmap`, ...)" => "models/functors.md",
          ],
+         "Tutorials" => [
+            "Linear Regression" => "tutorials/linear_regression.md",
+            "Custom Layers" => "models/advanced.md",  # TODO move freezing to Training
+         ],
          "Performance Tips" => "performance.md",
          "Flux's Ecosystem" => "ecosystem.md",
-         "Tutorials" => [  # TODO, maybe
-             "Custom Layers" => "models/advanced.md",  # TODO move freezing to Training
-         ],
     ],
     format = Documenter.HTML(
         sidebar_sitename = false,

docs/src/gpu.md

@@ -17,7 +17,7 @@ true
 
 Support for array operations on other hardware backends, like GPUs, is provided by external packages like [CUDA](https://github.com/JuliaGPU/CUDA.jl). Flux is agnostic to array types, so we simply need to move model weights and data to the GPU and Flux will handle it.
 
-For example, we can use `CUDA.CuArray` (with the `cu` converter) to run our [basic example](models/basics.md) on an NVIDIA GPU.
+For example, we can use `CUDA.CuArray` (with the `cu` converter) to run our [basic example](@ref man-basics) on an NVIDIA GPU.
 
 (Note that you need to have CUDA available to use CUDA.CuArray – please see the [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) instructions for more details.)
 

docs/src/index.md

@@ -16,9 +16,9 @@ Other closely associated packages, also installed automatically, include [Zygote
 
 ## Learning Flux
 
-The [quick start](models/quickstart.md) page trains a simple neural network.
+The [quick start](@ref man-quickstart) page trains a simple neural network.
 
-This rest of this documentation provides a from-scratch introduction to Flux's take on models and how they work, starting with [fitting a line](models/overview.md). 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.
+This rest of this documentation 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.
 
 Sections with 📚 contain API listings. The same text is avalable at the Julia prompt, by typing for example `?gpu`.
 

docs/src/models/activation.md

@@ -1,5 +1,4 @@
-
-# Activation Functions from NNlib.jl
+# [Activation Functions from NNlib.jl](@id man-activations)
 
 These non-linearities used between layers of your model are exported by the [NNlib](https://github.com/FluxML/NNlib.jl) package.
 

docs/src/models/advanced.md

@@ -1,4 +1,4 @@
-# Defining Customised Layers
+# [Defining Customised Layers](@id man-advanced)
 
 Here we will try and describe usage of some more advanced features that Flux provides to give more control over model building.
 
@@ -34,7 +34,7 @@ For an intro to Flux and automatic differentiation, see this [tutorial](https://
 
 ## Customising Parameter Collection for a Model
 
-Taking reference from our example `Affine` layer from the [basics](basics.md#Building-Layers-1).
+Taking reference from our example `Affine` layer from the [basics](@ref man-basics).
 
 By default all the fields in the `Affine` type are collected as its parameters, however, in some cases it may be desired to hold other metadata in our "layers" that may not be needed for training, and are hence supposed to be ignored while the parameters are collected. With Flux, it is possible to mark the fields of our layers that are trainable in two ways.
 

docs/src/models/functors.md

@@ -4,7 +4,7 @@ Flux models are deeply nested structures, and [Functors.jl](https://github.com/F
 
 New layers should be annotated using the `Functors.@functor` macro. This will enable [`params`](@ref Flux.params) to see the parameters inside, and [`gpu`](@ref) to move them to the GPU.
 
-`Functors.jl` has its own [notes on basic usage](https://fluxml.ai/Functors.jl/stable/#Basic-Usage-and-Implementation) for more details. Additionally, the [Advanced Model Building and Customisation](../models/advanced.md) page covers the use cases of `Functors` in greater details.
+`Functors.jl` has its own [notes on basic usage](https://fluxml.ai/Functors.jl/stable/#Basic-Usage-and-Implementation) for more details. Additionally, the [Advanced Model Building and Customisation](@ref man-advanced) page covers the use cases of `Functors` in greater details.
 
 ```@docs
 Functors.@functor

docs/src/training/optimisers.md

@@ -4,7 +4,7 @@ CurrentModule = Flux
 
 # Optimisers
 
-Consider a [simple linear regression](../models/basics.md). We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters `W` and `b`.
+Consider a [simple linear regression](@ref man-linear-regression). We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters `W` and `b`.
 
 ```julia
 using Flux

docs/src/training/training.md

@@ -36,12 +36,12 @@ Flux.Optimise.train!
 ```
 
 There are plenty of examples in the [model zoo](https://github.com/FluxML/model-zoo), and
-more information can be found on [Custom Training Loops](../models/advanced.md).
+more information can be found on [Custom Training Loops](@ref man-advanced).
 
 ## Loss Functions
 
-The objective function must return a number representing how far the model is from its target – the *loss* of the model. The `loss` function that we defined in [basics](../models/basics.md) will work as an objective.
-In addition to custom losses, a model can be trained in conjunction with
+The objective function must return a number representing how far the model is from its target – the *loss* of the model. The `loss` function that we defined in [basics](@ref man-basics) will work as an objective.
+In addition to custom losses, model can be trained in conjuction with
 the commonly used losses that are grouped under the `Flux.Losses` module.
 We can also define an objective in terms of some model:
 
@@ -64,11 +64,11 @@ At first glance, it may seem strange that the model that we want to train is not
 
 ## Model parameters
 
-The model to be trained must have a set of tracked parameters that are used to calculate the gradients of the objective function. In the [basics](../models/basics.md) section it is explained how to create models with such parameters. The second argument of the function `Flux.train!` must be an object containing those parameters, which can be obtained from a model `m` as `Flux.params(m)`.
+The model to be trained must have a set of tracked parameters that are used to calculate the gradients of the objective function. In the [basics](@ref man-basics) section it is explained how to create models with such parameters. The second argument of the function `Flux.train!` must be an object containing those parameters, which can be obtained from a model `m` as `Flux.params(m)`.
 
 Such an object contains a reference to the model's parameters, not a copy, such that after their training, the model behaves according to their updated values.
 
-Handling all the parameters on a layer-by-layer basis is explained in the [Layer Helpers](../models/basics.md) section. For freezing model parameters, see the [Advanced Usage Guide](../models/advanced.md).
+Handling all the parameters on a layer by layer basis is explained in the [Layer Helpers](@ref man-basics) section. Also, for freezing model parameters, see the [Advanced Usage Guide](@ref man-advanced).
 
 ```@docs
 Flux.params