대한민국의 시군구별 인구밀도를 코로플래스(Choropleth)로 시각화. 자세한 작성법은 D3를 이용한 시각적 스토리텔링 부록 C 참고.
- Author: Lucy Park
- License: Apache v2
Lucy Park e9t
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| { | |
| "nodes": [ | |
| {"label": "i0", "layer": 1}, | |
| {"label": "i1", "layer": 1}, | |
| {"label": "h0", "layer": 2}, | |
| {"label": "i2", "layer": 1}, | |
| {"label": "h1", "layer": 2}, | |
| {"label": "h2", "layer": 2}, | |
| {"label": "h3", "layer": 2}, | |
| {"label": "o0", "layer": 3} |
- 데이터 출처: http://m.wikitree.co.kr/main/news_view.php?id=217101
- 데이터 수집: 위 기사에서
crawl.py를 이용해 맛집 목록 수집 후 네이버 지도 API를 이용해서 좌표 정보 수집 - Author: Lucy Park
- License: Apache v2
다음은 2010년 11월, 대한민국 국회에서 발의된 의안 제1809890호의 원문에서 높은 빈도로 등장한 단어를 한국어 워드클라우드로 나타낸 것입니다.
(그리는 법 참고: http://lucypark.kr/courses/2015-dm/text-mining.html)
다양한 가격대의 서울시내 스시야를 시각화한 지도.
맛집 이름들이 겹치지 않게 하기 위해 forced layout을 적용했습니다.
- 데이터 수집: 수작업
- Author: Lucy Park
- License: Apache v2
e9t
/ mini_sequence_labeler.py
Created
August 31, 2017 00:40
— forked from hal3/mini_sequence_labeler.py
PyTorch implementation of a sequence labeler (POS taggger).
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| """ | |
| PyTorch implementation of a sequence labeler (POS taggger). | |
| Basic architecture: | |
| - take words | |
| - run though bidirectional GRU | |
| - predict labels one word at a time (left to right), using a recurrent neural network "decoder" | |
| The decoder updates hidden state based on: | |
| - most recent word |
e9t
/ labels_1024.tsv
Created
April 12, 2017 11:26
— forked from teamdandelion/labels_1024.tsv
TensorBoard: TF Dev Summit Tutorial
We can make this file beautiful and searchable if this error is corrected: No tabs found in this TSV file in line 0.
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| 7 | |
| 2 | |
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| 0 | |
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| 1 | |
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| 9 | |
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| 9 |
I hereby claim:
- I am e9t on github.
- I am e9t (https://keybase.io/e9t) on keybase.
- I have a public key whose fingerprint is A72B 93D1 701C 52C6 9ED2 9BE4 97DD 0810 6C12 59B1
To claim this, I am signing this object:
e9t
/ lda-advi-ae.ipynb
Created
June 29, 2016 23:41
— forked from taku-y/lda-advi-ae.ipynb
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