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YSDA Natural Language Processing course

  • Lecture and seminar materials for each week are in ./week* folders
  • Create cloud jupyter session from this repo - Binder
  • Telegram chat room (russian).
  • YSDA homework deadlines are listed in Anytask course page.
  • Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
  • Installing libraries and troubleshooting: this thread.

Syllabus

  • week01 Embeddings

    • Lecture: Word embeddings. Distributional semantics, LSA, Word2Vec, GloVe. Why and when we need them.
    • Seminar: Playing with word and sentence embeddings.
  • week02 Text classification

    • Lecture: Text classification. Classical approaches for text representation: BOW, TF-IDF. Neural approaches: embeddings, convolutions, RNNs
    • Seminar: Salary prediction with convolutional neural networks; explaining network predictions.
  • week03 Language Models

    • Lecture: Language models: N-gram and neural approaches; visualizing trained models
    • Seminar: Generating ArXiv papers with language models
  • week04 Seq2seq/Attention

    • Lecture: Seq2seq: encoder-decoder framework. Attention: Bahdanau model. Self-attention, Transformer. Pointer networks. Attention for analysis.
    • Seminar: Machine translation of hotel and hostel descriptions
  • week05 Structured Learning

    • Lecture: Structured Learning: structured perceptron, structured prediction, dynamic oracles, RL basics.
    • Seminar: POS tagging
  • week06 Expectation-Maximization

    • Lecture: Expectation-Maximization and Word Alignment Models
    • Seminar: Implementing expectation maximizaiton
  • week07 Machine translation

    • Lecture: Machine Translation: a review of the key ideas from PBMT, the application specific ideas that have developed in NMT over the past 3 years and some of the open problems in this area.
    • Seminar: presentations by students

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YSDA course in Natural Language Processing

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  • Jupyter Notebook 91.9%
  • Python 7.0%
  • Dockerfile 1.1%