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Deep Learning / NLP tutorial for Chatbot Developers
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Day_01 Relocate previous notebooks Sep 13, 2017
Day_02 Edit train default kwarg for postporcess pipeline Sep 17, 2017
Day_03 Add seq2seq attention Aug 1, 2017
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slides Add slides (pdf) Oct 9, 2017
Readme.md Welcoming PRs Oct 11, 2017

Readme.md

Deep leaning for Chatbot Developers

Contents

Day 01 Introduction to Chatbot (slideshare)

  • Introduction to NLP/Chatbot
  • Overview of Korean/English NLP Toolkits/Datasets
  • Tutorial (code)
    • Introduction to spaCy / gensim / konlpy / other Korean toolkits
    • Sentiment classification via TF-IDF (scikit-learn)
    • Chatbot Pipelining / Serving via Kakaotalk (flask) / Slack (slacker)

Day 02 Text Classification with CNN/RNN (slideshare)

  • CNN for text classification
    • Word CNN / Dynamic CNN / Char CNN / Very Deep CNN
  • RNN for text classification
    • Bidirectional RNN / Recursive NN / Tree LSTM / Dual Encoder LSTM
  • Advanced CNN/RNN architectures
    • QRNN / SRU / ByteNet / SliceNet / LSTM-CNNs-CRF
  • Tutorial (code)
    • Word-CNN for sentiment analysis
    • PyTorch Style Guide
    • TorchText Tutorial

Day 03 Conversation Modeling with Seq2Seq / Attention (slideshare)

  • Seq2Seq models for conversation modeling
    • Seq2Seq / Neural Conversation model / Diversity-prompting objective: MMI
  • Advanced Seq2Seq architectures
    • Show and Tell / HRED / VHRED / Personal based Neural Conversation model / Contextualized Word Vectors (CoVe)
  • Attention mechanism
    • Bahdanau / Luong
    • Global / Local
  • Advanced Attention architectures
    • Show, Attend and Tell / Pointer Networks / CopyNet / BiDAF / Transformer
  • Tutorial (code)
    • Seq2Seq with Attention for Machine Translation

Day 04 QA with External Memory (slideshare)

  • QA with External Memory
    • Memory Networks / End-to-End Memory Networks / Key-value Memory Networks / Neural Turing Machines
  • Advanced Memory architectures
    • DNC / Life-long memory Modules / Context-Sequence Memory Networks
  • Advanced Dialogue Architectures
    • MILABOT / Dialog based language learning / End-to-End Goal Oriented Dialog / Deep RL / Adversarial
  • Tutorial (code)
    • End-to-End Memory Networks for Question Answering (bAbI)

Dependencies

Python 3

  • Codes are written in Anacodna Python 3.6.
  • Package management via Conda or virtualenv is recommended.

ML / NLP

  • PyTorch
  • TorchText
  • spaCy
  • sckit-learn
  • gensim
  • konlpy (requires Jpype3)

Interactive / DataFrame / Plot

  • jupyter
  • pandas
  • matplotlib

Kakaotalk / Slack Bot

  • flask
  • websocket-client
  • beautifulsoup4
  • slacker
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