CS224n, winter, 2019习题内容及解答

贴一个Readme.md吧,大部分代码和习题解答我已经放到github上了,链接在此处

CS224n-winter19

Solutions for CS224n, winter, 2019.
Welcome to discuss problems appearing in assigments, please submit to issue.
Also take notes for the key point in lectures.
The solutions for assignment is written by Markdown in Assignments/written part.
 

  • Course page: https://web.stanford.edu/class/cs224n
  • Video page: https://www.youtube.com/watch?v=8rXD5-xhemo&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

w1

reading

  • note: Word Vectors I: Introduction, SVD and Word2Ve
  • Word2Vec Tutorial - The Skip-Gram Model
     

practice

  • coding: Assignment1
  • Gensim

w2

reading

  • note: Word Vectors II: GloVe, Evaluation and Trainin
  • gradient-notes
  • CS231n notes on backprop
  • review-differential-calculus
  • backprop_old
  • CS231n notes on network architectures

practice

  • coding: Assignment2
  • writing: Assignment2

w3

reading

  • note: Dependency Parsing
  • note: Language Models and Recurrent Neural Network
  • a3

practice

  • coding: Assignment3
  • writing: Assignment3

w4

reading

  • note: Machine Translation, Sequence-to-sequence and Attention
  • a4
  • read: Attention and Augmented Recurrent Neural Networks
  • read: Massive Exploration of Neural Machine Translation Architectures (practical advice for hyperparameter choices)

practice

  • coding: Assignment4
  • writing: Assignment4

key point for a4

How to understand pack_padded_sequence and pad_packed_sequence?
(Chinese ed)
(English ed)

w5

It has been long time for no updating…

reading

  • note: Machine Translation, Sequence-to-sequence and Attention
  • a4
  • read: Attention and Augmented Recurrent Neural Networks

practice

  • coding: Assignment5
  • writing: Assignment5

Final project

reading:

  • final-project-practical-tips
  • default-final-project-handout
  • project-proposal-instructions
  • Practical Methodology_Deep Learning book chapter
  • Highway Networks
  • Bidirectional Attention Flow for Machine Comprehension

practice:

  • anotate codes
  • train baseline

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