From 9c2ea5ec5abec09531a7f891bb0b853d72ad406a Mon Sep 17 00:00:00 2001 From: Dominik Jain Date: Wed, 6 Dec 2023 00:44:39 +0100 Subject: [PATCH] Reorder intro section --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 8e7e84bc2..e68ecc368 100644 --- a/README.md +++ b/README.md @@ -24,12 +24,6 @@ Through high levels of abstraction and integration, sensAI minimises overhead whilst retaining a high degree of **flexibility** for the implementation of custom solutions. -If you would normally use a library like scikit-learn on its own, -consider adding sensAI in order to - * gain flexibility, straightforwardly supporting a greater variety of models, - * increase the level of abstraction, cutting down on boilerplate, - * improve logging and tracking with minimal effort. - Some of sensAI's key benefits are: * **A unifying interface to a wide variety of model classes across frameworks** @@ -77,6 +71,12 @@ Some of sensAI's key benefits are: Gain the flexibility of specifying variations of your models and experiments with minimal code changes/extensions. +So if you would normally use a library like scikit-learn or XGBoost on its own, +consider adding sensAI in order to + * gain flexibility, straightforwardly supporting a greater variety of models, + * increase the level of abstraction, cutting down on boilerplate, + * improve logging and tracking with minimal effort. + While sensAI's main focus is on supervised and unsupervised machine learning, it also provides functionality for discrete optimisation and a wide range of general-purpose utilities that are frequently required in AI applications.