@@ -23,7 +23,7 @@ DGL is an easy-to-use, high performance and scalable Python package for deep lea
### A GPU-ready graph library
DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks.
DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks.
### A versatile tool for GNN researchers and practitioners
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@@ -32,7 +32,7 @@ DGL collects a rich set of [example implementations](https://github.com/dmlc/dgl
### Easy to learn and use
DGL provides a plenty of learning materials for all kinds of users from ML researcher to domain experts. The [Blitz Introduction to DGL](https://docs.dgl.ai/tutorials/blitz/index.html) is a 120-minute tour of the basics of graph machine learning. The [User Guide](https://docs.dgl.ai/guide/index.html) explains in more details the concepts of graphs as well as the training methodology. All of them include code snippets in DGL that are runnable and ready to be plugged into one’s own pipeline.
DGL provides plenty of learning materials for all kinds of users from ML researchers to domain experts. The [Blitz Introduction to DGL](https://docs.dgl.ai/tutorials/blitz/index.html) is a 120-minute tour of the basics of graph machine learning. The [User Guide](https://docs.dgl.ai/guide/index.html) explains in more details the concepts of graphs as well as the training methodology. All of them include code snippets in DGL that are runnable and ready to be plugged into one’s own pipeline.
Minjie Wang <wmjlyjemaine@gmail.com>