j-min / Dl For Chatbot
Deep Learning / NLP tutorial for Chatbot Developers
Stars: ✭ 221
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Deep leaning for Chatbot Developers
- Course Materials of Deep leaning for Chatbot Developers (Sep. 2017)
- Author: Jaemin Cho
- Pull Requests welcome :)
Contents
slideshare)
Day 01 Introduction to Chatbot (- 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)
slideshare)
Day 02 Text Classification with CNN/RNN (- 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
slideshare)
Day 03 Conversation Modeling with Seq2Seq / Attention (- 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
slideshare)
Day 04 QA with External Memory (- 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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