scikit-learn · cmarmo · Jul 8, 2020 · Jul 10, 2020 · Jul 10, 2020 · Jul 10, 2020
diff --git a/doc/themes/scikit-learn-modern/static/css/theme.css b/doc/themes/scikit-learn-modern/static/css/theme.css
@@ -76,6 +76,7 @@ a code {
 
 img {
    max-width: 100%;
+   border-radius: 0.25rem;
 }
 
 span.highlighted {
@@ -836,10 +837,6 @@ div.highlight:hover span.copybutton:hover {
     background-color: #20252B;
 }
 
-div.body img.align-center {
-  max-width: 800px;
-}
-
 div.body img {
     max-width: 100%;
     height: unset!important; /* Needed because sphinx sets the height */
@@ -1210,16 +1207,34 @@ div.sk-sponsor-div, div.sk-testimonial-div {
   align-items: center;
 }
 
-div.sk-sponsor-div-box, div.sk-testimonial-div-box {
+div.sk-sponsor-div-box, div.sk-testimonial-div-box,
+div.sk-doc-div-box {
   width: 100%;
 }
 
+div.sk-doc-div {
+  display: flex;
+  flex-wrap: wrap;
+  justify-content: center;
+}
+
+div.sk-doc-div-box {
+  padding: 0.30rem;
+  overflow: auto;
+}
+
 @media screen and (min-width: 500px) {
   div.sk-sponsor-div-box, div.sk-testimonial-div-box {
     width: 50%;
   }
 }
 
+@media screen and (min-width: 1200px) {
+  div.sk-doc-div-box {
+    width: 50%;
+  }
+}
+
 table.sk-sponsor-table tr, table.sk-sponsor-table tr:nth-child(odd) {
   border-style: none;
   background-color: white;

diff --git a/doc/tutorial/statistical_inference/finding_help.rst b/doc/tutorial/statistical_inference/finding_help.rst
@@ -27,6 +27,6 @@ Q&A communities with Machine Learning practitioners
 
 .. _`multiple subdomains for Machine Learning questions`: https://meta.stackexchange.com/q/130524
 
--- _'An excellent free online course for Machine Learning taught by Professor Andrew Ng of Stanford': https://www.coursera.org/learn/machine-learning
+- `An excellent free online course for Machine Learning taught by Professor Andrew Ng of Stanford <https://www.coursera.org/learn/machine-learning>`_
 
--- _'Another excellent free online course that takes a more general approach to Artificial Intelligence': https://www.udacity.com/course/intro-to-artificial-intelligence--cs271
+- `Another excellent free online course that takes a more general approach to Artificial Intelligence <https://www.udacity.com/course/intro-to-artificial-intelligence--cs271>`_
diff --git a/doc/tutorial/statistical_inference/model_selection.rst b/doc/tutorial/statistical_inference/model_selection.rst
@@ -180,23 +180,35 @@ scoring method.
 .. currentmodule:: sklearn.svm
 
 .. topic:: **Exercise**
-   :class: green
 
-   .. image:: /auto_examples/exercises/images/sphx_glr_plot_cv_digits_001.png
-        :target: ../../auto_examples/exercises/plot_cv_digits.html
-        :align: right
-        :scale: 90
+    .. raw :: html
+
+       <div class="sk-doc-div">
+         <div class="sk-doc-div-box">
+
+    On the digits dataset, plot the cross-validation score of a :class:`SVC`
+    estimator with an linear kernel as a function of parameter ``C`` (use a
+    logarithmic grid of points, from 1 to 10).
 
-   On the digits dataset, plot the cross-validation score of a :class:`SVC`
-   estimator with an linear kernel as a function of parameter ``C`` (use a
-   logarithmic grid of points, from 1 to 10).
+        .. literalinclude:: ../../auto_examples/exercises/plot_cv_digits.py
+            :lines: 13-23
 
-   .. literalinclude:: ../../auto_examples/exercises/plot_cv_digits.py
-       :lines: 13-23
+    .. raw :: html
+
+         </div>
+           <div class="sk-doc-div-box">
+
+    .. image:: /auto_examples/exercises/images/sphx_glr_plot_cv_digits_001.png
+        :target: ../../auto_examples/exercises/plot_cv_digits.html
+        :align: center
+        :scale: 90
 
-   **Solution:** :ref:`sphx_glr_auto_examples_exercises_plot_cv_digits.py`
+    .. raw :: html
 
+         </div>
+       </div>
 
+    **Solution:** :ref:`sphx_glr_auto_examples_exercises_plot_cv_digits.py`
 
 Grid-search and cross-validated estimators
 ============================================
@@ -273,7 +285,6 @@ These estimators are called similarly to their counterparts, with 'CV'
 appended to their name.
 
 .. topic:: **Exercise**
-   :class: green
 
    On the diabetes dataset, find the optimal regularization parameter
    alpha.

diff --git a/doc/tutorial/statistical_inference/putting_together.rst b/doc/tutorial/statistical_inference/putting_together.rst
@@ -11,16 +11,13 @@ Pipelining
 We have seen that some estimators can transform data and that some estimators
 can predict variables. We can also create combined estimators:
 
-.. image:: ../../auto_examples/compose/images/sphx_glr_plot_digits_pipe_001.png
-   :target: ../../auto_examples/compose/plot_digits_pipe.html
-   :scale: 65
-   :align: right
-
 .. literalinclude:: ../../auto_examples/compose/plot_digits_pipe.py
     :lines: 23-63
 
-
-
+.. image:: ../../auto_examples/compose/images/sphx_glr_plot_digits_pipe_001.png
+   :target: ../../auto_examples/compose/plot_digits_pipe.html
+   :scale: 65
+   :align: center
 
 Face recognition with eigenfaces
 =================================
@@ -40,20 +37,28 @@ The dataset used in this example is a preprocessed excerpt of the
 .. |eigenfaces| image:: ../../images/plot_face_recognition_2.png
    :scale: 50
 
-.. list-table::
-   :class: centered
+.. raw :: html
+
+   <div class="sk-doc-div">
+    <div class="sk-doc-div-box">
+
+     <h4>Prediction</h4>
+
+|prediction|
 
-   *
+.. raw :: html
 
-     - |prediction|
+    </div>
+      <div class="sk-doc-div-box">
 
-     - |eigenfaces|
+     <h4>Eigenfaces</h4>
 
-   *
+|eigenfaces|
 
-     - **Prediction**
+.. raw :: html
 
-     - **Eigenfaces**
+     </div>
+    </div>
 
 Expected results for the top 5 most represented people in the dataset::
 

diff --git a/doc/tutorial/statistical_inference/settings.rst b/doc/tutorial/statistical_inference/settings.rst
@@ -31,6 +31,11 @@ needs to be preprocessed in order to be used by scikit-learn.
 
 .. topic:: An example of reshaping data would be the digits dataset
 
+    .. raw :: html
+
+       <div class="sk-doc-div">
+         <div class="sk-doc-div-box">
+
     The digits dataset is made of 1797 8x8 images of hand-written
     digits ::
 
@@ -41,16 +46,25 @@ needs to be preprocessed in order to be used by scikit-learn.
         >>> plt.imshow(digits.images[-1], cmap=plt.cm.gray_r) #doctest: +SKIP
         <matplotlib.image.AxesImage object at ...>
 
-    .. image:: /auto_examples/datasets/images/sphx_glr_plot_digits_last_image_001.png
-        :target: ../../auto_examples/datasets/plot_digits_last_image.html
-        :align: left
-        :scale: 60
-
     To use this dataset with scikit-learn, we transform each 8x8 image into a
     feature vector of length 64 ::
 
         >>> data = digits.images.reshape((digits.images.shape[0], -1))
 
+    .. raw :: html
+
+         </div>
+         <div class="sk-doc-div-box">
+
+    .. image:: /auto_examples/datasets/images/sphx_glr_plot_digits_last_image_001.png
+        :target: ../../auto_examples/datasets/plot_digits_last_image.html
+        :align: center
+
+    .. raw :: html
+
+         </div>
+       </div>
+
 
 Estimators objects
 ===================