Make section titles consistent

docs/getting-started/advanced-usage.md

@@ -18,7 +18,7 @@ using one of Dask, Ray or Joblib. The first is used in
 the [influence][pydvl.influence] package whereas the other two
 are used in the [value][pydvl.value] package. 
 
-### Data Valuation
+### Data valuation
 
 For data valuation, pyDVL uses [joblib](https://joblib.readthedocs.io/en/latest/) for local
 parallelization (within one machine) and supports using
@@ -100,7 +100,7 @@ u = Utility(...)
 combinatorial_exact_shapley(u, config=config)
 ```
 
-### Influence Functions
+### Influence functions
 
 Refer to the [[scaling-influence-computations]] page for explanations
 about parallelization for Influence Function computations.

docs/getting-started/applications.md

@@ -23,7 +23,7 @@ comprehensive overview, along with concrete examples, please refer to the
 [Transferlab blog post]({{ transferlab.website }}blog/data-valuation-applications/)
 on this topic.
 
-## Data Engineering
+## Data engineering
 
 Some of the promising applications in data engineering include:
 

docs/getting-started/first-steps.md

@@ -1,17 +1,17 @@
 ---
-title: First Steps
+title: First steps
 alias: 
   name: first-steps
   text: First Steps
 ---
 
-# First Steps
+# First steps
 
 !!! Warning
     Make sure you have read [[getting-started#installation]] before using the library. 
     In particular read about which extra dependencies you may need.
 
-## Main Concepts
+## Main concepts
 
 pyDVL aims to be a repository of production-ready, reference implementations of
 algorithms for data valuation and influence functions. Even though we only
@@ -22,7 +22,7 @@ should be enough to get you started.
   computation and related methods.
 * [[influence-function]] for instructions on how to compute influence functions.
 
-## Running the Examples
+## Running the examples
 
 If you are somewhat familiar with the concepts of data valuation, you can start
 by browsing our worked-out examples illustrating pyDVL's capabilities either:
@@ -34,7 +34,7 @@ by browsing our worked-out examples illustrating pyDVL's capabilities either:
   have to install jupyter first manually since it's not a dependency of the
   library.
 
-## Advanced Usage
+## Advanced usage
 
 Refer to the [[advanced-usage]] page for explanations on how to enable
 and use parallelization and caching.

docs/getting-started/index.md

@@ -1,8 +1,8 @@
 ---
-title: Getting Started
+title: Getting started
 alias:
   name: getting-started
-  title: Getting Started
+  title: Getting started
 ---
 
 # Getting started

docs/getting-started/methods.md

@@ -7,7 +7,7 @@ alias:
 
 We currently implement the following methods:
 
-## Data Valuation
+## Data valuation
 
 - [**LOO**][pydvl.value.loo.compute_loo].
 
@@ -44,7 +44,7 @@ We currently implement the following methods:
 - [**Data-OOB**][pydvl.value.oob.compute_data_oob]
   [@kwon_dataoob_2023].
 
-## Influence Functions
+## Influence functions
 
 - [**CG Influence**][pydvl.influence.torch.CgInfluence].
   [@koh_understanding_2017].