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데이터 사이언스 SCHOOL
1
Project Description 1
GOAL
- RNN의 최신 version인 LSTM을 모델링 해보고 공부해 보고자 한다.
Data Preprocessing
- Stopword 와 Stemming을 사용하여 전처리를 진행해 보고자 했지만 이것만으로는
Loss function 값이 낮아지지 않아 다른 참가자들의 코드를 벤치마킹과 train set에 있는
문장들을 살펴보고 정규표현식으로 제거해 주거나 수정해 주었다.
Modeling
- Input -> Embedding(using Google corpus) -> LSTM(260개, 120차원) -> dropout ->
BatchNormalization
- loss='binary_crossentropy', optimizer='nadam' 사용
Kaggle_Quora_Text Analytics
개인 Project / 2017.05.23 ~ 2017.06.09
Abstract
RNN의 변형인 LSTM과 Word2Vect를 사용하여 2개의 문장을 비교하는 모델을
설계해보았다.
데이터 사이언스 SCHOOL
2
Project Description 2
Result
Limiation
- GPU를 사용하지 않아 Epoch 10번을 돌리는데 거의 25시간 가까이 걸림
- 최적화된 LSTM의 갯수 및 Dense를 파악하는게 목표였지만 CPU를 사용했기 때문에
최적화라고 말하기엔 무리가 있음
Kaggle_Quora_Text Analytics
개인 Project / 2017.05.23 ~ 2017.06.09

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Kaggle quora

  • 1. 데이터 사이언스 SCHOOL 1 Project Description 1 GOAL - RNN의 최신 version인 LSTM을 모델링 해보고 공부해 보고자 한다. Data Preprocessing - Stopword 와 Stemming을 사용하여 전처리를 진행해 보고자 했지만 이것만으로는 Loss function 값이 낮아지지 않아 다른 참가자들의 코드를 벤치마킹과 train set에 있는 문장들을 살펴보고 정규표현식으로 제거해 주거나 수정해 주었다. Modeling - Input -> Embedding(using Google corpus) -> LSTM(260개, 120차원) -> dropout -> BatchNormalization - loss='binary_crossentropy', optimizer='nadam' 사용 Kaggle_Quora_Text Analytics 개인 Project / 2017.05.23 ~ 2017.06.09 Abstract RNN의 변형인 LSTM과 Word2Vect를 사용하여 2개의 문장을 비교하는 모델을 설계해보았다.
  • 2. 데이터 사이언스 SCHOOL 2 Project Description 2 Result Limiation - GPU를 사용하지 않아 Epoch 10번을 돌리는데 거의 25시간 가까이 걸림 - 최적화된 LSTM의 갯수 및 Dense를 파악하는게 목표였지만 CPU를 사용했기 때문에 최적화라고 말하기엔 무리가 있음 Kaggle_Quora_Text Analytics 개인 Project / 2017.05.23 ~ 2017.06.09