Event  Date  Description  Course Materials  

Lecture  Jan 9  Introduction to NLP and Deep Learning [slides] 
Suggested Readings:  
Lecture  Jan 11  Word Vectors 1 [slides] 
Suggested Readings:  
A1 released  Jan 11  Assignment #1 released  [Assignment #1][ Written Solutions ]  
Lecture  Jan 16  Word Vectors 2 [slides] 
Suggested Readings:  
Lecture  Jan 18  Neural Networks [slides] 
Suggested Readings:  
Review  Jan 19  Python Refresher  [ slides ]  
Lecture  Jan 23  Backpropagation and Project Advice [slides] [lecture notes] 
Suggested Readings:  
Lecture  Jan 25  Introduction to TensorFlow [slides] [lecture code] 
Suggested Readings:  
A1 Due  Jan 25  Assignment #1 due  
A2 Released  Jan 25  Assignment #2 released  [Assignment #2] [ Written Solutions ]  
Lecture  Jan 30  Dependency Parsing [slides] 
Suggested Readings:


Lecture  Feb 1  Recurrent Neural Networks and Language Models [slides] 
Suggested Readings: [Ngram Language Models and Perplexity] [The Unreasonable Effectiveness of Recurrent Neural Networks] [Recurrent Neural Networks Tutorial] [Sequence Modeling: Recurrent and Recursive Neural Nets] 

DFP Released  Feb 1  Default Final Project released  
Lecture  Feb 6  Vanishing Gradients, Fancy RNNs [slides] 
Suggested Readings: [Understanding LSTM Networks] [Vanishing Gradients Example] 

Review  Feb 8  Midterm Review [slides] 
This year's midterm will be most similar to practice midterm 3 (the first two are from cs224d).
[practice midterm 1] [with solutions] [practice midterm 2] [with solutions] [practice midterm 3] [with solutions] 

Project Proposal Due  Feb 8  Final Project proposal due  Final Project Proposal  
A2 Due  Feb 8  Assignment #2 due  
Alternate Midterm  Feb 9  Alternate Midterm  
A3 Released  Feb 13  Assignment #3 released  Assignment #3 [ Written Solutions ]  
Midterm  Feb 13  Inclass midterm 
Location: Memorial Auditorium, Time: 4:30  5:50pm [Midterm] [Midterm Solutions] 

Lecture  Feb 15  Machine Translation, Seq2Seq and Attention [slides] 
Suggested Readings: [Statistical Machine Translation slides (see lectures 2/3/4)] [Statistical Machine Translation Book] [BLEU metric] [Original sequencetosequence NMT paper (also describes beam search)] [Earlier sequencetosequence speech recognition paper (includes detailed beam search alg)] [Original sequencetosequence + attention paper] [Guide to attention and other RNN augmentations] [Massive Exploration of Neural Machine Translation Architectures] 

Lecture  Feb 20  Advanced Attention [slides] 
Suggested Readings: [A Deep Reinforced Model for Abstractive Summarization] [Get To The Point: Summarization with PointerGenerator Networks] [BlackOut: Speeding up Recurrent Neural Network Language Models with very Large Vocabularies] [Achieving Open Vocabulary Neural Machine Translation with Hybrid WordCharacter Models] [QuasiRecurrent Neural Networks] 

Lecture  Feb 22  Transformer Networks and CNNs [slides] 
Suggested Readings: [Attention Is All You Need] [Layer Normalization] [Convolutional Neural Networks for Sentence Classification] [Improving neural network3s by preventing coadaptation of feature detectors] [A Convolutional Neural Network for Modelling Sentences] 

Lecture  Feb 27 
Coreference Resolution [slides] 
Suggested Readings: [Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution] [Improving Coreference Resolution by Learning EntityLevel Distributed Representations] [Endtoend Neural Coreference Resolution] [Coreference Demo] 

A3 Due  Feb 27  Assignment #3 due  
Milestone Due  Feb 28  Final project milestone due  Project Milestone  
Lecture  Mar 1  Tree Recursive Neural Networks and Constituency Parsing [slides] 

Lecture  Mar 6  Advanced Architectures and Memory Networks [slides] 

Lecture  Mar 8  Reinforcement Learning for NLP Guest Lecture [slides] 
Suggested Readings: [A Deep Reinforced Model for Abstractive Summarization] [DCN+: Mixed Objective and Deep Residual Coattention for Question Answering] [Deep Reinforcement Learning for Dialogue Generation] 

Lecture  Mar 13  Semisupervised Learning for NLP [slides] 
Suggested Readings: [SemiSupervised Sequence Learning] [Learned in Translation: Contextualized Word Vectors] [Deep Contextualized Word Representations] [Adversarial Training Methods for SemiSupervised Text Classification] 

Lecture  Mar 15  Future of NLP Models, Multitask Learning and QA Systems [slides] 

Final Project Due  Mar 18  Final project due  
Poster Presentation  Mar 21  Final project poster presentations 
5:308:30 McCaw Hall at the Alumni Center McCaw Hall at the Alumni Center More Details 