tweaks

docs/src/models/basics.md

@@ -211,7 +211,7 @@ m = Chain(x -> x^2, x -> x+1)
 m(5) # => 26
 ```
 
-## Layer helpers
+## Layer Helpers
 
 Flux provides a set of helpers for custom layers, which you can enable by calling
 

docs/src/models/overview.md

@@ -11,7 +11,7 @@ Under the hood, Flux uses a technique called automatic differentiation to take g
 
 Here's how you'd use Flux to build and train the most basic of models, step by step.
 
-## Make a Trivial Prediction
+### A Trivial Prediction
 
 This example will predict the output of the function `4x + 2`. Making such predictions is called "linear regression", and is really too simple to *need* a neural network. But it's a nice toy example.
 
@@ -26,7 +26,7 @@ actual (generic function with 1 method)
 
 This example will build a model to approximate the `actual` function.
 
-## Provide Training and Test Data
+## 1. Provide Training and Test Data
 
 Use the `actual` function to build sets of data for training and verification:
 
@@ -40,7 +40,7 @@ julia> y_train, y_test = actual.(x_train), actual.(x_test)
 
 Normally, your training and test data come from real world observations, but here we simulate them.
 
-## Build a Model to Make Predictions
+## 2. Build a Model to Make Predictions
 
 Now, build a model to make predictions with `1` input and `1` output:
 
@@ -85,7 +85,7 @@ julia> loss(x_train, y_train)
 
 More accurate predictions will yield a lower loss. You can write your own loss functions or rely on those already provided by Flux. This loss function is called [mean squared error](https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/mean-squared-error/). Flux works by iteratively reducing the loss through *training*.
 
-## Improve the Prediction
+## 3. Improve the Prediction
 
 Under the hood, the Flux [`Flux.train!`](@ref) function uses *a loss function* and *training data* to improve the *parameters* of your model based on a pluggable [`optimiser`](../training/optimisers.md):
 
@@ -150,7 +150,7 @@ Params([Float32[7.5777884], Float32[1.9466728]])
 
 The parameters have changed. This single step is the essence of machine learning.
 
-## Iteratively Train the Model
+## 3+. Iteratively Train the Model
 
 In the previous section, we made a single call to `train!` which iterates over the data we passed in just once. An *epoch* refers to one pass over the dataset. Typically, we will run the training for multiple epochs to drive the loss down even further. Let's run it a few more times:
 
@@ -168,7 +168,7 @@ Params([Float32[4.0178537], Float32[2.0050256]])
 
 After 200 training steps, the loss went down, and the parameters are getting close to those in the function the model is built to predict.
 
-## Verify the Results
+## 4. Verify the Results
 
 Now, let's verify the predictions: