https://github.com/clab/dynet_tutorial_examples

Tutorial on "Practical Neural Networks for NLP: From Theory to Code" at EMNLP 2016
https://github.com/clab/dynet_tutorial_examples

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Tutorial on "Practical Neural Networks for NLP: From Theory to Code" at EMNLP 2016

• Host: GitHub
• URL: https://github.com/clab/dynet_tutorial_examples
• Owner: clab
• Created: 2016-10-25T22:11:01.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2017-03-05T13:37:09.000Z (over 7 years ago)
• Last Synced: 2024-07-29T07:32:25.192Z (4 months ago)
• Language: Python
• Homepage:
• Size: 842 KB
• Stars: 432
• Watchers: 37
• Forks: 117
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# Practical Neural Networks for NLP

A tutorial given by [Chris Dyer](http://www.cs.cmu.edu/~cdyer/), [Yoav Goldberg](https://www.cs.bgu.ac.il/~yoavg/uni/), and [Graham Neubig](http://www.phontron.com/) 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](https://www.github.com/clab/dynet) toolkit.

* [Slides, part 1: Basics](http://phontron.com/slides/emnlp2016-dynet-tutorial-part1.pdf)

    * Computation graphs and their construction

    * Neural networks in DyNet

    * Recurrent neural networks

    * Minibatching

    * Adding new differentiable functions

* [Slides, part 2: Case studies in NLP](http://phontron.com/slides/emnlp2016-dynet-tutorial-part2.pdf)

    * Tagging with bidirectional RNNs and character-based embeddings

    * Transition-based dependency parsing

    * Structured prediction meets deep learning