1/e

Understanding exponentially weighted averages



from Understanding Exponentially Weighted Averages (C2W2L04)
 https://www.youtube.com/watch?v=NxTFlzBjS-4

RMSprop

So that's RMSprop, and similar to momentum, has the effects of damping out the oscillations in gradient descent, in mini-batch gradient descent. And allowing you to maybe use a larger learning rate alpha. And certainly speeding up the learning speed of your algorithm.

from Andrew Ng's lecture
https://www.youtube.com/watch?v=_e-LFe_igno

Setup Bluetooth through Terminal

터미널에서 bluetooth 페어링하기  (블루투스 마우스)
https://www.cnet.com/how-to/how-to-setup-bluetooth-on-a-raspberry-pi-3/

or

http://archive.is/tpEHO

1x1 Convolutions

1x1 Convolutions from Udacity

T표준점수

학생들 채점을 하다보니 필요한 표준점수..

http://blog.naver.com/PostView.nhn?blogId=oohyes0201&logNo=30031094911
http://archive.is/07qtY

Z-점수: 평균=0, 표준편차=1인 표준점수
        Z = (변수 - 평균) / 표준편차   <높은 점수 - 높은 성적>
        Z'= (평균 - 변수) / 표준편차    <낮은 점수 - 높은 성적>

 T-점수: 평균=50, 표준편차=10인 표준점수
        T = 10Z + 50    <높은 점수 - 높은 성적>
        T'= 10Z' + 50    <낮은 점수 - 높은 성적>

Virtualenv 환경 복사하기

On the first virtual env:

pip freeze > requirements.txt

On the second:

pip install -r requirements.txt

https://stackoverflow.com/a/11702289

process terminated with status -1 (Codeblocks)

Settings -> Environments

Terminal to launch console programs :
gnome-terminal -t $TITLE -x
인데, gnome-terminal 이 설치 안된 경우도 있음.

$ sudo apt-get install gnome-terminal

참고로 default는 다음과 같음
xterm -T $TITLE -e

햅틱스 워크샵

GNU screen 중단 (termination)

MEMS Microphone - INMP401 (ADMP401)

Sensor #002 - MEMS Microphone - INMP401 (ADMP401)

See more at
https://github.com/dalek7/Undergrad-2017Fall-IoT/tree/master/Processing%2BArduino/02-Serial-ADC-basic

when optimizing the cost function J

And so I find machine learning easier to think about when you have one set of tools for optimizing the cost function J, and when you're focusing on authorizing the cost function J. All you care about is finding w and b, so that J(w,b) is as small as possible. You just don't think about anything else other than reducing this.


https://www.coursera.org/lecture/deep-neural-network/other-regularization-methods-Pa53F

SimpleHTTPServer

이런게 buit-in으로 있었다니...

$ sudo python -m SimpleHTTPServer 80

Jupyter notebook from the remote client

Server (jupyter notebook 구동) :

(server)$ jupyter notebook --no-browser --port=8888

Client :

(client)$ ssh -N -f -L localhost:8888:localhost:8888 user@ipaddress of server(client)$ open -a safari http://localhost:8888

localhost:8888(로칼):localhost:8888(remote)
원격 포트를 바꾸고자할때 사용할수는 있음

예를들면
localhost:8888:localhost:8889
으로 설정하면 원격 8889를 local 8888로 (remote port 8889 to local port 8888)

Cygwin을 사용해도 됨

(client)$ ssh -N -f -L localhost:8888:localhost:8888 user@ipaddress of server

ssh 포트가 포워딩되는 경우
ssh 포트 지정하듯

ssh -N -f -L localhost:8888:localhost:8888 user@ip_address_server -p SSH_PORT_NUMBER

Token authentication 문제가 나오면 server에 접속해서
(server)$ jupyter notebook list 를 하고 보이는 토큰 키를 넣어주면 끝.

jupyter notebook에서 tensorflow 사용하기 (virtualenv)

우선 필요한것들 설치..


$ ipython kernelspec install-self --user
기본 kernel 설치

~/.local/share/jupyter/kernels$ ls
python2

$ cp -r python2 tensorflow

~/.local/share/jupyter/kernels/tensorflow$ vi kernel.json

 아래처럼 수정
~/.local/share/jupyter/kernels/tensorflow$ cat kernel.json
{
 "display_name": "tensorflow",
 "language": "python",
 "argv": [
  "/home/username/tensorflow/bin/python2.7",
  "-m",
  "ipykernel_launcher",
  "-f",
  "{connection_file}"
 ]
}

위의 /home/username/tensorflow/ 는 virtualenv로 tensorflow 설치 위치임

아래와 같이 tensorflow의 interpreter를 사용하도록 만들수 있음.