Minjoon Seo (seominjoon)

seominjoon

Geek Repo

Company:KAIST

Location:Seoul, Republic of Korea

Home Page:https://seominjoon.github.io

Twitter:@seo_minjoon

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Minjoon Seo's starred repositories

TensorFlow-Tutorials

Simple tutorials using Google's TensorFlow Framework

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nmtpy

nmtpy is a Python framework based on dl4mt-tutorial to experiment with Neural Machine Translation pipelines.

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DeepQA

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

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elasticsearch

Free and Open Source, Distributed, RESTful Search Engine

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wikiextractor

A tool for extracting plain text from Wikipedia dumps

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gensim

Topic Modelling for Humans

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Awesome-Korean-NLP

A curated list of resources for NLP (Natural Language Processing) for Korean

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OpenNMT

Open Source Neural Machine Translation in Torch (deprecated)

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yrsnlp-2016

Structured Neural Networks for NLP: From Idea to Code

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rc-data

Question answering dataset featured in "Teaching Machines to Read and Comprehend

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dl_tutorials

Deep learning tutorials (2nd ed.)

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rtb-papers

A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.

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dmn-tensorflow

Dynamic Memory Networks (https://arxiv.org/abs/1603.01417) in Tensorflow

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stat212b

Topics Course on Deep Learning UC Berkeley

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dynet

DyNet: The Dynamic Neural Network Toolkit

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vimrc

The ultimate Vim configuration (vimrc)

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awesome-torch

A curated list of awesome Torch tutorials, projects and communities

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deepframeworks

Evaluation of Deep Learning Frameworks

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caffe-tensorflow

Caffe models in TensorFlow

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treelstm

Tree-structured Long Short-Term Memory networks (http://arxiv.org/abs/1503.00075)

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VQA_LSTM_CNN

Train a deeper LSTM and normalized CNN Visual Question Answering model. This current code can get 58.16 on OpenEnded and 63.09 on Multiple-Choice on test-standard.

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VQAbaseline

Simple Baseline for Visual Question Answering

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awesome-rnn

Recurrent Neural Network - A curated list of resources dedicated to RNN

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fpinscala

Code, exercises, answers, and hints to go along with the book "Functional Programming in Scala"

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