Practical Neural Networks for NLP
A tutorial given by Chris Dyer, Yoav Goldberg, and Graham Neubig at EMNLP 2016 in Austin. The tutorial covers the basic of neural networks for NLP, and how to implement a variety of networks simply and efficiently in the DyNet toolkit.

 Computation graphs and their construction
 Neural networks in DyNet
 Recurrent neural networks
 Minibatching
 Adding new differentiable functions

Slides, part 2: Case studies in NLP
 Tagging with bidirectional RNNs and characterbased embeddings
 Transitionbased dependency parsing
 Structured prediction meets deep learning