More notebook fixes

notebooks/k_nearest_neighbors.livemd

@@ -47,7 +47,7 @@ key = Nx.Random.key(42)
 >
 ```
 
-## Introduction
+## Use cases
 
 This notebook will cover the three primary applications of k-nearest Neighbors: classification, regression, and anomaly detection. Let's get started with a practical example. Imagine just moved to a new city, and you're here for the first time. Since you're an active person, you'd like to find a nearby gym with good facilities. What would you do? You'd probably start by searching for gyms on online maps. The search results might look something like this:
 

notebooks/linear_regression.livemd

@@ -16,7 +16,7 @@ Mix.install([
 ])
 ```
 
-## Introduction
+## Setup
 
 In the livebook, we will cover the typical use cases of linear regression on practical examples.
 
@@ -50,7 +50,7 @@ key = Nx.Random.key(42)
 
 <!-- livebook:{"branch_parent_index":0} -->
 
-## Linear Regression on Synthetic Data
+## Linear regression on synthetic data
 
 Before we dive into real-life use cases of linear regression, we start with a simpler one. We will generate data with a linear pattern and then use `Scholar.Linear.LinearRegression` to compute regression.
 
@@ -225,7 +225,7 @@ x_b |> Nx.LinAlg.pinv() |> Nx.dot(y)
 
 <!-- livebook:{"branch_parent_index":0} -->
 
-## Polynomial Regression on Synthetic Data
+## Polynomial regression on synthetic data
 
 Before moving on to a more complex example, this section will briefly show how to use another regression method. While not strictly linear, the approach and calculations that go into it is similar enough that it makes sense for it to be explained alongside linear regression.
 

notebooks/mds.livemd

@@ -30,7 +30,7 @@ Nx.global_default_options(compiler: EXLA)
 
 <!-- livebook:{"branch_parent_index":0} -->
 
-## Swiss Roll
+## Swiss roll
 
 Multidimensional scaling (MDS) seeks a low-dimensional representation of the data in which the distances respect well the distances in the original high-dimensional space.
 
@@ -1081,7 +1081,7 @@ As we see, MDS collapsed one dimension and what we see is similar to a cross sec
 
 <!-- livebook:{"branch_parent_index":0} -->
 
-## Digits Dataset
+## Digits dataset
 
 In the next section we change dataset to Digits dataset. It consists of almost 1800 8x8 images with digits.
 

notebooks/nearest_neighbors.livemd

@@ -121,7 +121,7 @@ x = Scholar.Preprocessing.StandardScaler.fit_transform(x)
 {x, y}
 ```
 
-Once the data is preprocessed, we can move on to the KDTree model. First, we need to set up the model using `Scholar.Neighbors.KDTree.fit`. This method initializes the tree structure for further processing.
+Once the data is preprocessed, we can move on to the KDTree model. First, we need to set up the model using `Scholar.Neighbors.KDTree.fit/2`. This method initializes the tree structure for further processing.
 
 ```elixir
 num_neighbors = 6