Neural Networks
for NLP

Content:

• Recurrent Networks
• Vanishing Gradient and LSTMs
• Strengths and Weaknesses of Recurrence in Sentence Modeling
• Pre-training for RNNs

Reading Material

• Required Reading (for quiz): Goldberg Book Chapter 14-15
• Other Reading: Goldberg Book Chapter 16 (will be covered in class)
• Reference: RNNs (Elman 1990)
• Reference: LSTM (Hochreiter and Schmidhuber 1997)
• Reference: Variants of LSTM (Greff et al. 2015)
• Reference: GRU (Cho et al. 2014)
• Reference: Visualizing Recurrent Nets (Karpathy et al. 2015)
• Reference: Learning Syntax from Translation (Shi et al. 2016)
• Reference: Learning Sentiment from LMs (Radford et al. 2017)
• Reference: Optimizing RNNs with CuDNN (Appleyard 2015)

Slides: RNN Slides
Sample Code: RNN Code Examples


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