readme adjustments

README.md

@@ -27,16 +27,16 @@ I assume you have at least installed PyTorch, know Python, and understand Tensor
 
 * http://pytorch.org/ For installation instructions
 * [Deep Learning with PyTorch: A 60-minute Blitz](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) to get started with PyTorch in general
-* [jcjohnson's PyTorch examples](https://github.com/jcjohnson/pytorch-examples) for an in depth overview (including custom modules and autograd functions)
 * [Introduction to PyTorch for former Torchies](https://github.com/pytorch/tutorials/blob/master/Introduction%20to%20PyTorch%20for%20former%20Torchies.ipynb) if you are a former Lua Torch user
+* [jcjohnson's PyTorch examples](https://github.com/jcjohnson/pytorch-examples) for a more in depth overview (including custom modules and autograd functions)
 
 You should know about Recurrent Neural Networks and how they work:
 
 * [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) shows a bunch of real life examples
 * [Deep Learning, NLP, and Representations](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) for an overview on word embeddings and RNNs for NLP
-* [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) is about LSTMs specifically but also informative about RNNs in general
+* [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) is about LSTMs work specifically, but also informative about RNNs in general
 
-And for more, read the papers that introduced these topics:
+And for more, read the papers that introduced many of these topics:
 
 * [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](http://arxiv.org/abs/1406.1078)
 * [Sequence to Sequence Learning with Neural Networks](http://arxiv.org/abs/1409.3215)