Merge pull request #264 from davidshumway/patch-1

Update README.md

README.md

@@ -30,7 +30,7 @@
 
 <p align="justify">The library consists of various dynamic and temporal geometric deep learning, embedding, and spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemological forecasting, sharing economy, energy production and web traffic management domains. Finally, you can also create your own datasets.</p>
 
-The package interfaces well with [Pytorch Lightning](https://pytorch-lightning.readthedocs.io) which allows training on CPUs, single and multiple GPUs out-of-the-box. Take a look at this [introductory example](https://github.com/benedekrozemberczki/pytorch_geometric_temporal/blob/master/examples/recurrent/lightning_example.py) of using PyTorch Geometric Temporal with Pytorch Lighning.
+The package interfaces well with [Pytorch Lightning](https://pytorch-lightning.readthedocs.io) which allows training on CPUs, single and multiple GPUs out-of-the-box. Take a look at this [introductory example](https://github.com/benedekrozemberczki/pytorch_geometric_temporal/blob/master/examples/recurrent/lightning_example.py) of using PyTorch Geometric Temporal with Pytorch Lightning.
 
 We also provide detailed examples for each of the [recurrent](https://github.com/benedekrozemberczki/pytorch_geometric_temporal/tree/master/examples/recurrent) models and [notebooks](https://github.com/benedekrozemberczki/pytorch_geometric_temporal/tree/master/notebooks) for the attention based ones.