Create a getting started section and add a new linear regression example

Author: Saransh-cpp

docs/Project.toml

@@ -2,9 +2,12 @@
 BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
 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"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 Documenter = "0.26"

docs/make.jl

@@ -1,17 +1,20 @@
-using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers
+using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers, Plots, MLDatasets, Statistics
 
 
 DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive = true)
 
 makedocs(
-    modules = [Flux, NNlib, Functors, MLUtils, BSON, Optimisers],
+    modules = [Flux, NNlib, Functors, MLUtils, BSON, Optimisers, Plots, MLDatasets, Statistics],
     doctest = false,
     sitename = "Flux",
     pages = [
         "Home" => "index.md",
+        "Getting Started" => [
+            "Overview" => "getting_started/overview.md",
+            "Basics" => "getting_started/basics.md",
+            "Linear Regression" => "getting_started/linear_regression.md",
+        ],
         "Building Models" => [
-            "Overview" => "models/overview.md",
-            "Basics" => "models/basics.md",
             "Recurrence" => "models/recurrence.md",
             "Model Reference" => "models/layers.md",
             "Loss Functions" => "models/losses.md",

docs/src/models/basics.md renamed to docs/src/getting_started/basics.md

@@ -221,4 +221,4 @@ Flux.@functor Affine
 
 This enables a useful extra set of functionality for our `Affine` layer, such as [collecting its parameters](../training/optimisers.md) or [moving it to the GPU](../gpu.md).
 
-For some more helpful tricks, including parameter freezing, please checkout the [advanced usage guide](advanced.md).
+For some more helpful tricks, including parameter freezing, please checkout the [advanced usage guide](../models/advanced.md).

docs/src/getting_started/linear_regression.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](getting_started/basics.md) 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/models/overview.md renamed to docs/src/getting_started/overview.md

@@ -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](../getting_started/basics.md#Building-Layers-1).
 
 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/gpu.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](../getting_started/linear_regression.md). We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters `W` and `b`.
 
 ```julia
 using Flux

docs/src/models/advanced.md

@@ -40,8 +40,8 @@ more information can be found on [Custom Training Loops](../models/advanced.md).
 
 ## 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](../getting_started/basics.md) 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](../getting_started/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)`.
 
 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](../getting_started/basics.md) section. Also, for freezing model parameters, see the [Advanced Usage Guide](../models/advanced.md).
 
 ```@docs
 Flux.params