https://github.com/j-min/dl-for-chatbot

Deep Learning / NLP tutorial for Chatbot Developers
https://github.com/j-min/dl-for-chatbot

chatbot deep-learning nlp pytorch tutorial

Last synced: 7 months ago
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Deep Learning / NLP tutorial for Chatbot Developers

• Host: GitHub
• URL: https://github.com/j-min/dl-for-chatbot
• Owner: j-min
• Created: 2017-07-24T05:41:56.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-10-11T04:05:42.000Z (over 8 years ago)
• Last Synced: 2025-05-15T16:42:22.377Z (11 months ago)
• Topics: chatbot, deep-learning, nlp, pytorch, tutorial
• Language: Jupyter Notebook
• Homepage:
• Size: 14.8 MB
• Stars: 234
• Watchers: 20
• Forks: 66
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md

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README

          
# Deep leaning for Chatbot Developers

- Course Materials of [Deep leaning for Chatbot Developers](http://www.fastcampus.co.kr/data_seminar_chatbot/) (Sep. 2017)

- Author: [Jaemin Cho](mailto:heythisischo@gmail.com)

- Pull Requests welcome :)

## Contents

### Day 01 Introduction to Chatbot ([slideshare](https://www.slideshare.net/JaeminCho6/dl-chatbot-seminar-day-01-80593691))

- 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](https://www.slideshare.net/JaeminCho6/dl-chatbot-seminar-day-02))

- 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](https://www.slideshare.net/JaeminCho6/dl-chatbot-seminar-day-03))

- 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](https://www.slideshare.net/JaeminCho6/dl-chatbot-seminar-day-04))

- 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