improve readability

docs/source/installation.rst

@@ -110,18 +110,21 @@ Step 2: Install ``ivadomed``
 
         .. tab:: NVIDIA GPU Support
 
-            PyTorch is an integral part of ``ivadomed`` which ships 
-            CUDA 10.2 and CUDA 11.1 runtime by default.
+            PyTorch, an integral part of ``ivadomed``, ships
+            CUDA 10.2 and CUDA 11.1 runtime by default with its
+            installation binaries. Thus, to accelerate ``ivadomed``
+            on with CUDA 10.2 on a Linux system, you'd just need
+            to have GPUs installed with an `NVIDIA driver version >=440.33 
+            <https://docs.nvidia.com/deploy/cuda-compatibility/index.html#minor-version-compatibility>`_.
+            And, for CUDA 11.1 you'd rather need an upgraded NVIDIA
+            driver version >=450.
 
-            Ampere-based GPUs (with a `Compute Capability <https://developer.nvidia.com/cuda-gpus>`_
-            of 8.x) only work with CUDA>=11.1. Although CUDA 11.1 is
-            backward compatible with older hardware, CUDA 10.2 is
-            preferred if available.
+            .. note::
 
-            To accelerate ``ivadomed`` with CUDA 10.2 on a Linux system, you'd
-            need to have GPUs installed with an `NVIDIA driver version >=440.33 
-            <https://docs.nvidia.com/deploy/cuda-compatibility/index.html#minor-version-compatibility>`_.
-            And, for CUDA 11.1 you'd need an upgraded NVIDIA driver version >=450.
+                Ampere-based GPUs (with a `Compute Capability <https://developer.nvidia.com/cuda-gpus>`_
+                of 8.x) only work with CUDA>=11.1. Although CUDA 11.1 is
+                backward compatible with older hardware, CUDA 10.2 is
+                preferred if available.
 
             To verify the NVIDIA driver version, just look in ``/sys`` by 
             executing the command ``cat /sys/module/nvidia/version`` and you'll find