MyLtYkRiTiK/dl_in_nlp_2019

Taking together Stanford cs224n course with support of iPavlov team.

dl_in_nlp_2019

Taking together Stanford cs224n course with support of iPavlov team.

News: https://t.me/dlinnlp

Chat: https://t.me/joinchat/B-HNhhRwNwBnSgYIkOhyPA

Piazza: http://piazza.com/mipt/spring2019/ipavlov1

Access code: ipavlov1

Syllabus: https://docs.google.com/spreadsheets/d/16oB-WgeHqJEm29Er_Zu3X1mQRXfOFidqGnNP0cbbAuo/

Google calendar: https://clck.ru/FBsQs

Community articles links: https://www.mendeley.com/community/dl-in-nlp-course/

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Week 1

• Lecture 2 | Word Vector Representations (word2vec): https://youtu.be/ERibwqs9p38
• Quizz before 07.02.2019 : https://goo.gl/forms/9EZmQhqfhicW0yaO2
• Quizz answers : Evernote Link
• Seminar materials:
  • Part 1. Conversational Artificial Intelligence.: https://youtu.be/3nKhzlfaOTE
  • Part 2. Course intro: https://youtu.be/U_1xdGUQZ5o
  • Part 3. Word vector representations : https://youtu.be/juDdkybtTv0
  • Slides Part 2 : https://bit.ly/2Gi9V1z
  • Slides Part 3 : https://bit.ly/2WQRtSR
• Additional materials:
  • Lecture 1 | Natural Language Processing with Deep Learning: https://youtu.be/OQQ-W_63UgQ
  • Lecture 3 | GloVe: Global Vectors for Word Representation: https://youtu.be/ASn7ExxLZws
  • Word2Vec Tutorial - The Skip-Gram Model: http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
  • Efficient Estimation of Word Representations in Vector Space: https://arxiv.org/pdf/1301.3781.pdf
  • Distributed Representations of Words and Phrases and their Compositionality: https://arxiv.org/pdf/1310.4546.pdf

Week 2

• Lecture 4 | Word Window Classification and Neural Networks: https://youtu.be/uc2_iwVqrRI
• Lecture 5 (before 48 minute) | Backpropagation and Project Advice: https://youtu.be/isPiE-DBagM
• Quizz before 14.02.2019 : https://goo.gl/forms/9yDB1KAojvpEABtf2
• Seminar materials:
  • Neural networks Part 1 : https://youtu.be/92Ctk9OzlDg
• Additional materials:
  • (in Russian) Линейные модели классификации и регрессии: https://habr.com/post/323890/
  • Intro in Convolutional Neural Networks for Visual Recognition: http://cs231n.github.io/neural-networks-1

Week 3

• Lecture 5 (cs231n) | Neural Networks Part 2: https://youtu.be/gYpoJMlgyXA
• (Optional) Lecture 4 (cs231n) | Backpropagation, Neural Networks Part 1: https://youtu.be/i94OvYb6noo
• Quizz before 28.02.2019: https://goo.gl/forms/0dVAtBdUUmeJU7NB2
• Seminar materials:
  • Neural networks Part 2 : https://youtu.be/1zv1IJAS9r4
  • Slides : https://docs.google.com/presentation/d/1f_-1g0bTp8gvX300HUa2aEUXPXq9LUgnM1zHygM1W10/
• Additional materials:
  • (in Russian) Регуляризация нейронных сетей: https://youtu.be/Zz98nDE5b8E
  • Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift : https://arxiv.org/abs/1502.03167
  • How Does Batch Normalization Help Optimization? https://arxiv.org/abs/1805.11604
  • Machine Learning Glossary: https://clck.ru/FFZ2x

Week 4

• Lecture 8 | Recurrent Neural Networks and Language Models : https://youtu.be/Keqep_PKrY8
• Lecture 9 (after 40 minute) | Machine Translation and Advanced Recurrent LSTMs and GRUs: https://youtu.be/QuELiw8tbx8?t=2471
• Quizz before 14.03.2019: https://goo.gl/forms/h6Ugof9QENxAAvEG2
• Seminar materials:
  • Recurrent Neural Networks and Language Models Part 1 : https://drive.google.com/drive/folders/1-4KBREKificOTissVN7ueNXR8JJYDlqy
  • Recurrent Neural Networks and Language Models Part 2 : https://youtu.be/Ms3eOk14Uyc
  • Slides : https://docs.google.com/presentation/d/1f_-1g0bTp8gvX300HUa2aEUXPXq9LUgnM1zHygM1W10/
• Additional materials:
  • CS 231n Python & NumPy Tutorial : https://clck.ru/FKKEy
  • 100 numpy exercises: https://github.com/rougier/numpy-100
  • The Unreasonable Effectiveness of Recurrent Neural Networks : http://karpathy.github.io/2015/05/21/rnn-effectiveness/
  • The Matrix Calculus You Need For Deep Learning : https://arxiv.org/abs/1802.01528

Week 5

• Deep contextualized word representations : https://arxiv.org/abs/1802.05365
• Universal Language Model Fine-tuning for Text Classification : https://arxiv.org/abs/1801.06146
• ELMo helps to further improve your sentence embeddings : https://towardsdatascience.com/elmo-helps-to-further-improve-your-word-embeddings-c6ed2c9df95f
• Introducing state of the art text classification with universal language models : http://nlp.fast.ai/
• The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) : https://jalammar.github.io/illustrated-bert
• Lecture 13 | Convolutional Neural Networks : https://youtu.be/Lg6MZw_OOLI
• Seminar materials:
  • Transfer Learning in NLP : https://youtu.be/aPNf1IRwqN0
  • Slides : https://docs.google.com/presentation/d/1rApVsEi-VDyfqIcfoulYTPppR2yXHDV_X_gYPsq4My4/

Week 6

• Lecture 8 (2019) | Sequence-to-sequence models and Attention : https://youtu.be/7m6noV5-l1E
• The Annotated Encoder-Decoder with Attention : https://clck.ru/FQ8gR