CS224n_winter2019_notes_and_assignments

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CS224n_winter2019_notes_and_assignments

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CS224n_learning_notes_and_assignments( winter,2019)

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- - exploring_word_vectors.ipynb
  - README.txt
- - collect_submission.sh
  - env.yml
  - get_datasets.sh
  - run.py
  - sgd.py
  - word2vec.py
  - word_vectors.png
  - written_part.html
  - written_part.md
  - written_part.pdf
- - collect_submission.sh
  - parser_model.py
  - parser_transitions.py
  - run.py
  - written_part.md
- - __init__.py
  - collect_submission.sh
  - gpu_requirements.txt
  - local_env.yml
  - model_embeddings.py
  - nmt_model.py
  - README.md
  - run.py
  - run.sh
  - sanity_check.py
  - utils.py
  - vocab.py
- - __init__.py
  - char_decoder.py
  - cnn.py
  - collect_submission.sh
  - gpu_requirements.txt
  - highway.py
  - local_env.yml
  - model_embeddings.py
  - nmt_model.py
  - run.py
  - run.sh
  - sanity_check.py
  - utils.py
  - vocab.py
- - exploring_word_vectors.ipynb
  - README.txt
- - collect_submission.sh
  - env.yml
  - get_datasets.sh
  - run.py
  - sgd.py
  - word2vec.py
- - collect_submission.sh
  - parser_model.py
  - parser_transitions.py
  - run.py
- - __init__.py
  - collect_submission.sh
  - gpu_requirements.txt
  - local_env.yml
  - model_embeddings.py
  - nmt_model.py
  - README.md
  - run.py
  - run.sh
  - sanity_check.py
  - utils.py
  - vocab.py
- - __init__.py
  - char_decoder.py
  - cnn.py
  - collect_submission.sh
  - gpu_requirements.txt
  - highway.py
  - local_env.yml
  - model_embeddings.py
  - nmt_model.py
  - run.py
  - run.sh
  - sanity_check.py
  - utils.py
  - vocab.py
README.md

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# Course name:  
**CS224n**: Natural Language Processing with Deep Learning , Stanford / Winter 2019
[Official website](http://web.stanford.edu/class/cs224n/)

# Lecture resources

## Lecture 1: Introduction and Word Vectors
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture01-wordvecs1.pdf)

[video](https://www.youtube.com/watch?v=8rXD5-xhemo)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes01-wordvecs1.pdf)

## Lecture 2: Word Vectors and Word Senses
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture02-wordvecs2.pdf)

[video](https://www.youtube.com/watch?v=kEMJRjEdNzM&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=2)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes02-wordvecs2.pdf)

## Lecture 3: Word Window Classification, Neural Networks, and Matrix Calculus 
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture03-neuralnets.pdf)

[video](https://www.youtube.com/watch?v=8CWyBNX6eDo)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes03-neuralnets.pdf)

## Lecture 4: Backpropagation 
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture04-backprop.pdf)

[video](https://www.youtube.com/watch?v=yLYHDSv-288&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=4)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes03-neuralnets.pdf)

## Lecture 5: Dependency Parsing
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture05-dep-parsing.pdf)

[video](https://www.youtube.com/watch?v=nC9_RfjYwqA&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=5)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes04-dependencyparsing.pdf)

## Lecture 6: Language Models and RNNs
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture06-rnnlm.pdf)

[video](https://www.youtube.com/watch?v=iWea12EAu6U&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=6)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes05-LM_RNN.pdf)

## Lecture 7: Vanishing Gradients, Fancy RNNs
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture07-fancy-rnn.pdf)

[video](https://www.youtube.com/watch?v=QEw0qEa0E50&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=7)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes05-LM_RNN.pdf)

## Lecture 8: Translation, Seq2Seq, Attention
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture08-nmt.pdf)

[video](https://www.youtube.com/watch?v=7m6noV5-l1E&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=8)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes06-NMT_seq2seq_attention.pdf)

## Lecture 9: Practical Tips for Projects
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture09-final-projects.pdf)

[video](https://www.youtube.com/watch?v=fyqm8fRDgl0&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=9)

[official notes](http://web.stanford.edu/class/cs224n/readings/final-project-practical-tips.pdf)

## Lecture 10: Question Answering
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture10-QA.pdf)

[video](https://www.youtube.com/watch?v=yIdF-17HwSk&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=10)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes07-QA.pdf)

## Lecture 11: Convolutional Networks for NLP
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture11-convnets.pdf)

[video](https://www.youtube.com/watch?v=EAJoRA0KX7I&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=11)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes08-CNN.pdf)

## Lecture 12: Subword Models
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture12-subwords.pdf)

[video](https://www.youtube.com/watch?v=9oTHFx0Gg3Q&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=12)

## Lecture 13: Contextual Word Embeddings
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture13-contextual-representations.pdf)

[video](https://www.youtube.com/watch?v=S-CspeZ8FHc&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=13)

## Lecture 14: Transformers and Self-Attention
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture14-transformers.pdf)

[video](https://www.youtube.com/watch?v=5vcj8kSwBCY&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=14)

## Lecture 15: Natural Language Generation
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture15-nlg.pdf)

[video](https://www.youtube.com/watch?v=4uG1NMKNWCU&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=15)

## Lecture 16: Coreference Resolution
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture16-coref.pdf)

[video](https://www.youtube.com/watch?v=i19m4GzBhfc&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=16)

## Lecture 17: Multitask Learning
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture17-multitask.pdf)

[video](https://www.youtube.com/watch?v=M8dsZsEtEsg&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=17)

## Lecture 18: Constituency Parsing, TreeRNNs
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture18-TreeRNNs.pdf)

[video](https://www.youtube.com/watch?v=6Z4A3RSf-HY&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=18)

[official notes](http://web.stanford.edu/class/cs224n/readings/cs224n-2019-notes09-RecursiveNN_constituencyparsing.pdf)

## Lecture 19: Bias in AI
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture19-bias.pdf)

[video](https://www.youtube.com/watch?v=XR8YSRcuVLE&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=19)

## Lecture 20: Future of NLP + Deep Learning
[slides](http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture20-future.pdf)

[video](https://www.youtube.com/watch?v=3wWZBGN-iX8&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=20)

# Assignment solutions

## Homework 1
[official file](https://github.com/lrs1353281004/CS224n_learning_notes/tree/master/homework_official/homework1)

[my solution](https://github.com/lrs1353281004/CS224n_learning_notes/tree/master/homework_my_solution/homework1) [**Finished!**]

## Homework 2
[official file](https://github.com/lrs1353281004/CS224n_learning_notes/tree/master/homework_official/homework2)

[handout](http://web.stanford.edu/class/cs224n/assignments/a2.pdf)

[written part:my answer](https://github.com/lrs1353281004/CS224n_winter2019_notes_and_assignments/blob/master/homework_my_solution/homework2/written_part.pdf) [**Finished!**]

[coding part:my solution](https://github.com/lrs1353281004/CS224n_learning_notes/tree/master/homework_my_solution/homework2) [**Finished!**]

## Homework 3
[official file](https://github.com/lrs1353281004/CS224n_winter2019_notes_and_assignments/tree/master/homework_official/homework3)

[handout](http://web.stanford.edu/class/cs224n/assignments/a3.pdf)

[written part:my answer](https://github.com/lrs1353281004/CS224n_winter2019_notes_and_assignments/blob/master/homework_my_solution/homework3/written_part.md) [**Finished!**]

[coding part:my solution](https://github.com/lrs1353281004/CS224n_winter2019_notes_and_assignments/tree/master/homework_my_solution/homework3) [**Finished!**]

## Homework 4
[official file](https://github.com/lrs1353281004/CS224n_winter2019_notes_and_assignments/tree/master/homework_official/homework4)

[handout](http://web.stanford.edu/class/cs224n/assignments/a4.pdf)

[coding part:my solution](https://github.com/lrs1353281004/CS224n_winter2019_notes_and_assignments/tree/master/homework_my_solution/homework4) [**Finished!**]

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