Study

2018년 5월 24일 목요일

파이썬 자료형

https://codehabitude.com/2013/12/24/python-objects-mutable-vs-immutable/

https://stackoverflow.com/questions/5541324/immutable-numpy-array
넘파이 어레이는 기본 mutable 이다.

오토인코더에대해서

https://d2.naver.com/news/0956269

(강의 1/3) http://tv.naver.com/v/3185672
(강의 2/3) http://tv.naver.com/v/3185730
(강의 3/3) http://tv.naver.com/v/3186249

(발표 자료) https://www.slideshare.net/NaverEngineering/ss-96581209

2018년 5월 18일 금요일

https://seaborn.pydata.org/tutorial/distributions.html

f, axes = plt.subplots(2, 2, figsize=(14, 14), sharex=True)

sns.heatmap(similarities, vmax=1.0 , vmin = 0.0 , square=True , ax = axes[0,0])
sns.distplot(histo , hist = True , bins = 10 , rug = True , ax = axes[0,1] )
plt.show()

기본적인 패턴이다.

다음 링크는 페어 플롯 이란 것아ㅣ다

https://towardsdatascience.com/visualizing-data-with-pair-plots-in-python-f228cf529166

데이터 비주얼 라이즈에 관한 링크1

https://cacm.acm.org/magazines/2010/6/92482-a-tour-through-the-visualization-zoo/fulltext

2018년 5월 17일 목요일

차원에 대해서

http://terryum.io/korean/2016/05/05/FeatureSelection_KOR/
차원 축소에 관해 . latent space 에 관한 이야기도 있다.

https://arxiv.org/pdf/1601.07996.pdf

차원 축소 기술 모음 !! 매우 좋다.
http://www.cs.cmu.edu/~efros/courses/AP06/presentations/melchior_isomap_demo.pdf

2018년 5월 15일 화요일

jupyterlab 단축키 지정법

commands 에 Notebook operations 에 restart kernel and run all cells 라는 것을

단축키 지정 하고 싶다.

먼저 https://github.com/jupyterlab/jupyterlab/blob/master/packages/notebook-extension/src/index.ts#L224

여기에 보면 command 안의 명령들의 명칭들을 볼 수 있다.

export
	const restartRunAll = 'notebook:restart-run-all';

이렇게 되어있다.

"notebook:@@여기에는 소문자오 command 안의 내용을 쓴다. 스페이스는 - 로": {
"command": @@ 여기에 위의 '' 안의 내용을 써준다.

이렇게

command 에는 Restart Run All 로 되어있으므로 restart-run-all 이라고

"notebook:restart-run-all": {
"command": "notebook:restart-run-all",
"keys": [
"Shift 2"
],
"selector": "[data-jp-kernel-user]:focus"
},

---- 내 설정
{
"kernelmenu:restart": {
"command": "kernelmenu:restart",
"keys": [
"Shift 1"
],
"selector": "[data-jp-kernel-user]:focus"
},

"notebook:restart-run-all": {
"command": "notebook:restart-run-all",
"keys": [
"Shift 2"
],
"selector": "[data-jp-kernel-user]:focus"
},

"notebook:notebook:run-all-above": {
"command": "notebook:run-all-above",
"keys": [
"Shift 3"
],
"selector": "[data-jp-kernel-user]:focus"
},

"notebook:close-and-shutdown": {
"command": "notebook:close-and-shutdown",
"keys": [
"Shift 4"
],
"selector": "[data-jp-kernel-user]:focus"
},

"notebook:collapse-selected-outputs": {
"command": "notebook:hide-cell-outputs",
"keys": [
"Q"
],
"selector": ".jp-Notebook:focus"
},

"notebook:expand-selected-outputs": {
"command": "notebook:show-cell-outputs",
"keys": [
"Q",
"Q"
],
"selector": ".jp-Notebook:focus"
},

"notebook:collapse-selected-code": {
"command": "notebook:hide-cell-code",
"keys": [
"W"
],
"selector": ".jp-Notebook:focus"
},

"notebook:expand-selected-code": {
"command": "notebook:show-cell-code",
"keys": [
"W",
"W"
],
"selector": ".jp-Notebook:focus"
},

"notebook:expand-all-inpouts": {
"command": "notebook:show-all-cell-inputs",
"keys": [
"E",
"E"
],
"selector": ".jp-Notebook:focus"
},

"notebook:collapse-all-inputs": {
"command": "notebook:hide-all-cell-inputs",
"keys": [
"E"
],
"selector": ".jp-Notebook:focus"
},

"notebook:expand-all-code": {
"command": "notebook:show-all-cell-code",
"keys": [
"R",
"R"
],
"selector": ".jp-Notebook:focus"
},

"notebook:collapse-all-code": {
"command": "notebook:hide-all-cell-code",
"keys": [
"R"
],
"selector": ".jp-Notebook:focus"
},

"notebook:change-to-cell-heading-1": {
"command": "notebook:change-cell-to-heading-1",
"keys": [

],
"selector": ".jp-Notebook:focus"
},

"notebook:change-to-cell-heading-2": {
"command": "notebook:change-cell-to-heading-1",
"keys": [

],
"selector": ".jp-Notebook:focus"
},
"notebook:change-to-cell-heading-3": {
"command": "notebook:change-cell-to-heading-1",
"keys": [

],
"selector": ".jp-Notebook:focus"
},
"notebook:change-to-cell-heading-4": {
"command": "notebook:change-cell-to-heading-1",
"keys": [

],
"selector": ".jp-Notebook:focus"
},
"notebook:change-to-cell-heading-5": {
"command": "notebook:change-cell-to-heading-1",
"keys": [

],
"selector": ".jp-Notebook:focus"
},

"notebook:change-cell-to-raw": {
"command": "notebook:change-cell-to-raw",
"keys": [

],
"selector": ".jp-Notebook:focus"
}

}

2018년 5월 1일 화요일

딥러닝 정보

- 오브젝트 디텍트 관련 정리 블로그
https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

- one show learning
https://blog.lunit.io/tag/one-shot-learning/

- 트리
https://ratsgo.github.io/machine%20learning/2017/03/26/tree/

- CNN 주요 모델
https://ratsgo.github.io/deep%20learning/2017/10/09/CNNs/

- 딥러닝 스터디 정보
http://deeplearningstudy.github.io/doc_deeplearning_paper.html

- 2017 딥러닝 정보
http://www.wildml.com/2017/12/ai-and-deep-learning-in-2017-a-year-in-review/

- 2017 딥러닝 토픽
https://iclr.cc/archive/www/2017.html

- 2017 최고의 논문에 관한 논의
https://www.reddit.com/r/MachineLearning/comments/6oyg1w/r_cvpr_2017_best_paper_awards/

- 영상처리 카이스트 강좌 - 최적화?
http://kooc.kaist.ac.kr/optimization2017

- 레딧 - 머신러닝 강좌
https://www.reddit.com/r/learnmachinelearning/

- 상받은 논문 소개 블로그
https://stair.center/archives/601

- http로 딥러닝 런 사이트 만들기
https://www.linkedin.com/pulse/complete-guide-build-convnet-http-based-application-using-ahmed-gad

- 2018 이미지 트랜스포트 유명 논문
https://arxiv.org/abs/1804.04732

- 논문 리뷰 (설명 잘되있다.)
https://blog.lunit.io/category/paper-review/

- 이상한 딥러닝 관련 비디오
http://www.evolvingai.org/deep-learning

-딥러닝 관련 웃긴 글
http://kr.deductiontheory.com/2017/01/blog-post.html

-score
http://blog.acronym.co.kr/557

- 인셉션 모델 자세한 설명
https://hacktilldawn.com/2016/09/25/inception-modules-explained-and-implemented/

- 여러 슬라이드 모음
https://www.slideshare.net/ssuser06e0c5?utm_campaign=profiletracking&utm_medium=sssite&utm_source=ssslideview

- 텐서플로우 최신 기능 리뷰 블로그
https://hiseon.me/2018/04/15/tensorflow-dataset/

2018년 4월 12일 목요일

quiver_engine 설치 오류 해결 방법

https://github.com/keplr-io/quiver/issues/32

agree with upstair, detail desrcription:

search keyword "quiver“ at pypi webside (https://pypi.python.org/pypi);
download one of quiver package name "quiver_engine 0.1.4.1.4";
unzip it on local disk and "cd ....\quiver_engine 0.1.4.1.4\”;
just run "pip install -e .", it will auto call setup.py, then quiver_engine installed without any problem;
run python under dos "cmd";
just for test, type "from quiver_engine impor server" and no error is reported;
just for use in keras, look detail on function server.lunch, type "help(quiver_engine.server.lanuch", you will find it suggest only 5 parameter like below and NEEDN'T CLASSES MEAN STD TOP these 4 parameters!!!"

help(server.launch)
Help on function launch in module quiver_engine.server:
launch(model, temp_folder='./tmp', input_folder='./', port=5000, html_base_dir=None)

it you add codes below, you MUST first make dir you need first!
server.launch(model = model,temp_folder='./quiver/tmp',input_folder='./quiver/input_imgs',port=5000)
9, run codes with launch function in pycharm or python, quiver_engine will OPEN A WEBPAGE show your images (you FIRST copy to input_folder before run codes) and select it to see feature maps in your diagram show also on web page's left frame.
if it run OK as mentioned in step 9, you trains will BLOCKED by quiver_engine!