Address review

docs/first-steps.rst

@@ -6,22 +6,22 @@ First steps
 
 The purpose of this guide is to illustrate the possible uses of geomstats.
 
-**Install geomstats.**
+**INSTALL GEOMSTATS**
 
 From a terminal (OS X & Linux), you can install geomstats and its requirements with ``pip3`` as follows::
 
     pip3 install -r requirements.txt
     pip3 install geomstats
 
-**Choose the backend.**
+**CHOOSE THE BACKEND**
 
-You can choose your backend, by setting the environment variable GEOMSTATS_BACKEND to numpy, tensorflow or pytorch. By default, numpy is used. You should only use the numpy backend for examples with visualizations.
+You can choose your backend by setting the environment variable ``GEOMSTATS_BACKEND`` to ``numpy``, ``tensorflow`` or ``pytorch``. By default, the numpy backend is used. You should only use the numpy backend for examples with visualizations.
 
 .. code-block:: bash
 
     export GEOMSTATS_BACKEND=numpy
 
-**First examples.**
+**FIRST EXAMPLES**
 
 We illustrate here the use of `geomstats` to generalize learning
 algorithms to Riemannian manifolds.
@@ -58,7 +58,7 @@ Each algorithm can be used with any of the manifolds and metric
 implemented in the package.
 
 
-**Next steps.**
+**NEXT STEPS**
 
 You can find more examples in the repository "examples" of geomstats. You can run them from the command line as follows.
 

docs/index.rst

@@ -11,12 +11,12 @@ such as manifolds and Riemannian metrics, with an object-oriented approach.
 
 The module `learning` implements statistics and learning algorithms for data
 on manifolds. The code is object-oriented and classes inherit from
-Scikit-Learn base classes and mixin.
+scikit-learn's base classes and mixins.
 
 To learn how to use `geomstats`, visit the page :ref:`first_steps`.
 To contribute to `geomstats` visit the page :ref:`contributing`.
 
-**Quick install**
+**QUICK INSTALL**
 
 .. code-block:: bash