reorder readme, add more recommended reading

Author: spro

Diff for: README.md

@@ -9,61 +9,72 @@ Learn PyTorch with project-based tutorials. These tutorials demonstrate modern t
 Applying recurrent neural networks to natural language tasks, from classification to generation.
 
 * [Classifying Names with a Character-Level RNN](https://github.com/spro/practical-pytorch/blob/master/char-rnn-classification/char-rnn-classification.ipynb)
-* *WIP* [Generating Shakespeare with a Character-Level RNN](https://github.com/spro/practical-pytorch/blob/master/char-rnn-generation/char-rnn-generation.ipynb)
+* [Generating Shakespeare with a Character-Level RNN](https://github.com/spro/practical-pytorch/blob/master/char-rnn-generation/char-rnn-generation.ipynb)
 * [Generating Names with a Conditional Character-Level RNN](https://github.com/spro/practical-pytorch/blob/master/conditional-char-rnn/conditional-char-rnn.ipynb)
-* 🔥 [Translation with a Sequence to Sequence Network and Attention](https://github.com/spro/practical-pytorch/blob/master/seq2seq-translation/seq2seq-translation.ipynb)
+* [Translation with a Sequence to Sequence Network and Attention](https://github.com/spro/practical-pytorch/blob/master/seq2seq-translation/seq2seq-translation.ipynb)
 * [Exploring Word Vectors with GloVe](https://github.com/spro/practical-pytorch/blob/master/glove-word-vectors/glove-word-vectors.ipynb)
 * *WIP* Sentiment Analysis with a Word-Level RNN and GloVe Embeddings
-* *WIP* Sentence Similarity with a Word-Level Autoencoder
-* *WIP* Intent Parsing with Recursive Application of Recurrent Neural Networks
 
-#### Series 2: RNNs for timeseries
+#### Series 2: RNNs for timeseries data
 
-* *WIP* Predicting future events with an RNN
+* *WIP* Predicting discrete events with an RNN
+
+## Get Started
+
+The quickest way to run these on a fresh Linux or Mac machine is to install [Anaconda](https://www.continuum.io/anaconda-overview):
+```
+curl -LO https://repo.continuum.io/archive/Anaconda3-4.3.0-Linux-x86_64.sh
+bash Anaconda3-4.3.0-Linux-x86_64.sh
+```
+
+Then install PyTorch:
+
+```
+conda install pytorch -c soumith
+```
+
+Then clone this repo and start Jupyter Notebook:
+
+```
+git clone http://github.com/spro/practical-pytorch
+cd practical-pytorch
+jupyter notebook
+```
 
 ## Recommended Reading
 
-I assume you have at least installed PyTorch, know Python, and understand Tensors:
+### PyTorch basics
 
 * http://pytorch.org/ For installation instructions
+* [Offical PyTorch tutorials](http://pytorch.org/tutorials/) for more tutorials (some of these tutorials are included there)
 * [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
 * [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:
+### Recurrent Neural Networks
 
 * [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 work specifically, but also informative about RNNs in general
 
-And for more, read the papers that introduced many of these topics:
+### Machine translation
 
 * [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)
-* [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473)
-* [A Neural Conversational Model](http://arxiv.org/abs/1506.05869)
 
-## Get Started
+### Attention models
 
-The quickest way to run these on a fresh Linux or Mac machine is to install [Anaconda](https://www.continuum.io/anaconda-overview):
-```
-curl -LO https://repo.continuum.io/archive/Anaconda3-4.3.0-Linux-x86_64.sh
-bash Anaconda3-4.3.0-Linux-x86_64.sh
-```
+* [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473)
+* [Effective Approaches to Attention-based Neural Machine Translation](https://arxiv.org/abs/1508.04025)
 
-Then install PyTorch:
+### Other RNN uses
 
-```
-conda install pytorch -c soumith
-```
+* [A Neural Conversational Model](http://arxiv.org/abs/1506.05869)
 
-Then clone this repo and start Jupyter Notebook:
+### Other PyTorch tutorials
+
+* [Deep Learning For NLP In PyTorch](https://github.com/rguthrie3/DeepLearningForNLPInPytorch)
 
-```
-git clone http://github.com/spro/practical-pytorch
-cd practical-pytorch
-jupyter notebook
-```
 ## Feedback
 
 If you have ideas or find mistakes [please leave a note](https://github.com/spro/practical-pytorch/issues/new).