DOC update rendering of package names

Author: TomDLT

README.rst

@@ -21,12 +21,12 @@ To explore these tutorials, one can:
   `website <https://gallantlab.github.io/voxelwise_tutorials/>`_ (recommended)
   (not online yet, ask Tom the local link)
 - run the Python scripts located in the `tutorials <tutorials>`_ directory
-- run the ``jupyter`` notebooks located in the
+- run the Jupyter notebooks located in the
   `tutorials/notebooks <tutorials/notebooks>`_ directory
 
 To run the tutorials yourself, first download this repository, and install the
 dependencies (see below). Then, run either the Python scripts or the
-``jupyter`` notebooks located in the "tutorials" directory. The tutorials are
+Jupyter notebooks located in the "tutorials" directory. The tutorials are
 best explored in order, starting with the "Movies 3T tutorial".
 
 Helper Python package

doc/index.rst

@@ -13,7 +13,7 @@ This website contains tutorials describing how to use the
 
 The tutorials consist of Python scripts, which are rendered in a `gallery of
 examples <_auto_examples/index.html>`_. Each Python script is also available as
-a ``jupyter`` notebook (non-rendered). The tutorials are best explored in
+a Jupyter notebook (non-rendered). The tutorials are best explored in
 order, starting with the "Movies 3T tutorial".
 
 To run the tutorials yourself, we recommend to download the project on GitHub
@@ -23,7 +23,7 @@ the GitHub repository contains a Python package called ``voxelwise_tutorials``,
 which contains useful fonctions to download the data sets, load the files,
 process the data, and visualize the results. Install instructions are available
 `here <voxelwise_package.html>`_. Then, run either the Python scripts or the
-``jupyter`` notebooks located in the "tutorials" directory.
+Jupyter notebooks located in the "tutorials" directory.
 
 Tutorials
 ---------

tutorials/movies_3T/README.rst

@@ -19,10 +19,10 @@ This tutorial implements different voxelwise models:
 - a ridge model with motion-energy features as described in [2]_.
 - a banded-ridge model with both feature spaces as described in [3]_.
 
-**Scikit-learn API:**
-These tutorials use ``scikit-learn`` to define the preprocessing steps, the
-modeling pipeline, and the cross-validation scheme. If you are not familiar
-with scikit-learn API, we recommend the `getting started guide
+**Scikit-learn API:** These tutorials use `scikit-learn
+<https://github.com/scikit-learn/scikit-learn>`_ to define the preprocessing
+steps, the modeling pipeline, and the cross-validation scheme. If you are not
+familiar with the scikit-learn API, we recommend the `getting started guide
 <https://scikit-learn.org/stable/getting_started.html>`_. We also use a lot of
 the scikit-learn terminology, which is explained in great details in the
 `glossary of common terms and API elements

tutorials/notebooks/movies_3T/README.rst

@@ -19,10 +19,10 @@ This tutorial implements different voxelwise models:
 - a ridge model with motion-energy features as described in [2]_.
 - a banded-ridge model with both feature spaces as described in [3]_.
 
-**Scikit-learn API:**
-These tutorials use ``scikit-learn`` to define the preprocessing steps, the
-modeling pipeline, and the cross-validation scheme. If you are not familiar
-with scikit-learn API, we recommend the `getting started guide
+**Scikit-learn API:** These tutorials use `scikit-learn
+<https://github.com/scikit-learn/scikit-learn>`_ to define the preprocessing
+steps, the modeling pipeline, and the cross-validation scheme. If you are not
+familiar with the scikit-learn API, we recommend the `getting started guide
 <https://scikit-learn.org/stable/getting_started.html>`_. We also use a lot of
 the scikit-learn terminology, which is explained in great details in the
 `glossary of common terms and API elements