| 일 | 월 | 화 | 수 | 목 | 금 | 토 |
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |||
| 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| 12 | 13 | 14 | 15 | 16 | 17 | 18 |
| 19 | 20 | 21 | 22 | 23 | 24 | 25 |
| 26 | 27 | 28 | 29 | 30 | 31 |
- SQL
- 데이터분석
- 딥러닝
- 기계학습
- Hadoop
- pandas
- R그래프
- 주가예측
- 데이터처리
- 빅데이터
- AI
- CNN
- R프로그래밍
- HIVE
- 데이터
- 자연어처리
- 빅데이터처리
- ggplot
- lstm
- 데이터시각화
- 하둡
- Python
- word2vec
- 그래프
- 머신러닝
- R시각화
- r
- NLP
- 그래프시각화
- Deeplearning
- Today
- Total
목록NLP (4)
욱이의 냉철한 공부
[논문 요약] RCN : Predict Stock Price with Financial News Based on Recurrent Convolutional Neural Network, 2017
* 논문 Predict Stock Price with Financial News Based on Recurrent Convolutional Neural Network, 2017 1. 입력 데이터 - 입력 데이터 대상 대만증시(1개) : Taiwan Stock Exchange(TWSE) 기업(4개) : TSMC, Hon Hai Precision Industry(Foxconn Technology Group), China Steel Corporation(CSC), Graffe King Bio => 각각 독립적인 모델링 설계 - 입력 데이터 종류 : 일 단위 일 단위의 거래데이터(종가)와 텍스트데이터(뉴스제목) 사용 - 입력 데이터 기간 1. 종가데이터 : 2007-06-21 ~ 2017-02-13, 2,397..
* Word Representation 관점 (Word Embedding) 1. Discrete Representation : Local Representation 1) One - hot Vector - One - hot Vector 2) Count Based - Bag of Words (BoW) - Document-Term Matrix (DTM) - (TDM) - Term Frequency-Inverse Document Frequency (TF - IDF) - N-gram Language Model (N-gram) 2. Continuous Representation 1) Prediction Based (Distributed Representation) - Neural Network Language ..
[NLP 개념정리] Word Representation : 카운트 기반 단어표현
* Word Representation 분류체계 1. Discrete Representation : Local Representation 1) One - hot Vector - One - hot Vector 2) Count Based - Bag of Words (BoW) - Document-Term Matrix (DTM) - (TDM) - Term Frequency-Inverse Document Frequency (TF - IDF) - N-gram Language Model (N-gram) 2. Continuous Representation 1) Prediction Based (Distributed Representation) - Neural Network Language Model (NNLM) or..
[NLP 개념정리] Word Embedding : GloVe
* 자료출저 및 참고논문 - 논문 GloVe : 2014, Global Vectors for Word Representation * Word Representation 분류체계 1. Discrete Representation : Local Representation 1) One - hot Vector - One - hot Vector 2) Count Based - Bag of Words (BoW) - Document-Term Matrix (DTM) - (TDM) - Term Frequency-Inverse Document Frequency (TF - IDF) - N-gram Language Model (N-gram) 2. Continuous Representation 1) Prediction Base..