Docs: Improve index/welcome page

docs/index.rst

@@ -11,54 +11,55 @@ for the implementation of custom solutions.
 
 Some of sensAI's key benefits are:
 
-  * **A unifying interface to a wide variety of model classes across frameworks**
+* **A unifying interface to a wide variety of model classes across frameworks**
 
-    Apply the same principles to a wide variety of models, whether they are
-    neural networks, tree ensembles or non-parametric models – without
-    losing the ability of exploiting each model's particular strengths.
+  Apply the same principles to a wide variety of models, whether they are
+  neural networks, tree ensembles or non-parametric models – without
+  losing the ability of exploiting each model's particular strengths.
 
-    sensAI supports models based on PyTorch, scikit-learn, XGBoost and
-    other libraries out of the box.
-    Support for custom models can straightforwardly be established.
+  sensAI supports models based on PyTorch, scikit-learn, XGBoost and
+  other libraries out of the box.
+  Support for custom models can straightforwardly be established.
 
-  * **Adaptive, composable data processing pipelines**
+* **Adaptive, composable data processing pipelines**
 
-    Modularise data pre-processing steps and features generation, representing
-    the properties of features explicitly.
+  Modularise data pre-processing steps and features generation, representing
+  the properties of features explicitly.
 
-      * For each model, select a suitable subset of features, composing the
-        the desired feature generators in order to obtain an initial
-        input pipeline.
-      * Transform the features into representations that are optimised for
-        the model at hand.
-        Some of the respective transformations can be automatically derived from
-        the properties associated with features, others can be manually
-        designed to exploit a model's specific capabilities (e.g. a tensor-based
-        representation of complex, non-tabular data for neural networks).
+  * For each model, select a suitable subset of features, composing the
+    the desired feature generators in order to obtain an initial
+    input pipeline.
 
-    Strongly associate pipelines with models in order to avoid errors and
-    gain the flexibility of supporting highly heterogeneous models within
-    a single framework, bridging the gap to production along the way.
+  * Transform the features into representations that are optimised for
+    the model at hand.
+    Some of the respective transformations can be automatically derived from
+    the properties associated with features, others can be manually
+    designed to exploit a model's specific capabilities (e.g. a tensor-based
+    representation of complex, non-tabular data for neural networks).
 
-  * **Fully integrated solutions for canonical tasks**
+  Strongly associate pipelines with models in order to avoid errors and
+  gain the flexibility of supporting highly heterogeneous models within
+  a single framework, bridging the gap to production along the way.
 
-    Do away with boilerplate code by using high-level interfaces for model
-    evaluation, model selection or feature selection.
-    Log and track all relevant parameters as well as results along the way,
-    using file-based logging or tracking frameworks such as MLflow.
+* **Fully integrated solutions for canonical tasks**
 
-  * **Declarative semantics**
+  Do away with boilerplate code by using high-level interfaces for model
+  evaluation, model selection or feature selection.
+  Log and track all relevant parameters as well as results along the way,
+  using file-based logging or tracking frameworks such as MLflow.
 
-    Through its high level of abstraction, sensAI achieves largely
-    declarative semantics: Focus on what to do rather than how to do it.
+* **Declarative semantics**
 
-    Eschew the notion of external configuration for a single task, making
-    your high-level code read like configuration instead.
-    Gain the flexibility of specifying variations of your models and experiments
-    with minimal code changes/extensions.
+  Through its high level of abstraction, sensAI achieves largely
+  declarative semantics: Focus on what to do rather than how to do it.
 
+  Eschew the notion of external configuration for a single task, making
+  your high-level code read like configuration instead.
+  Gain the flexibility of specifying variations of your models and experiments
+  with minimal code changes/extensions.
 
- For a quick overview of its main features, please refer to the README file on GitHub.
+
+For a quick overview of sensAI's main features, please refer to the `README file on GitHub <https://github.com/opcode81/sensAI/blob/develop/README.md>`_.
 
 
 Indices and Tables