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[Doc] Fixed grammatical typos in README (dmlc#4246)
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Co-authored-by: Minjie Wang <[email protected]>
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alxwen711 and jermainewang authored Jul 14, 2022
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Expand Up @@ -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

Expand All @@ -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.

### Scalable and efficient

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