Sungju Kim goddoe

## ubuntu_mount_unmount_disk.sh


# 1. find disks
sudo fdisk -l

# Sample of fdisk result
# Disk /dev/sda: 3.7 TiB, 4000787030016 bytes, 7814037168 sectors
# Units: sectors of 1 * 512 = 512 bytes
# Sector size (logical/physical): 512 bytes / 512 bytes
# I/O size (minimum/optimal): 512 bytes / 512 bytes

## make_python3_as_default.sh
ln `which python3` $(dirname `which python3`)/python
ln `which pip3` $(dirname `which pip3`)/pip

## check_firewall.sh
sudo ufw status

## install_docker.sh
# reference : https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/

# Remove old Docker
sudo apt-get remove docker docker-engine docker.io

# Add repository
sudo apt-get update

sudo apt-get install \
    apt-transport-https \

## update_packages.R
library("cranlike")
update_PACKAGES(dir="path/to/repos", type="source")

## gist:befb5be2f857fd36fafbace3e1ba5b6d
from multiprocessing.dummy import Pool
from sklearn.cluster import KMeans
import functools


...

pool = Pool()
results = pool.map(functools.partial(find_cluster, kmeans=kmeans, X=X), clusters)

## git_diff_exclude_specific_files.sh
git diff -- . ':(exclude)*.ipynb'

## simple_http_server_for_python3.sh
python3 -m http.server

## export_port_at_docker.txt
Method 1: Using Docker file
EXPOSE 5000-9999
Method 2: Using Run commands
docker run –expose=5000-9999
or
docker run -p 5000-9999:5000-9999

## generative_vs_discriminative.txt
reference: https://stackoverflow.com/questions/879432/what-is-the-difference-between-a-generative-and-discriminative-algorithm (by benhamner)

Let's say you have input data x and you want to classify the data into labels y. A generative model learns the joint probability distribution p(x,y) and a discriminative model learns the conditional probability distribution p(y|x) - which you should read as "the probability of y given x".

Here's a really simple example. Suppose you have the following data in the form (x,y):

(1,0), (1,0), (2,0), (2, 1)

p(x,y) is


	# 1. find disks
	sudo fdisk -l

	# Sample of fdisk result
	# Disk /dev/sda: 3.7 TiB, 4000787030016 bytes, 7814037168 sectors
	# Units: sectors of 1 * 512 = 512 bytes
	# Sector size (logical/physical): 512 bytes / 512 bytes
	# I/O size (minimum/optimal): 512 bytes / 512 bytes
	ln `which python3` $(dirname `which python3`)/python
	ln `which pip3` $(dirname `which pip3`)/pip
	# reference : https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/

	# Remove old Docker
	sudo apt-get remove docker docker-engine docker.io

	# Add repository
	sudo apt-get update

	sudo apt-get install \
	apt-transport-https \
	library("cranlike")
	update_PACKAGES(dir="path/to/repos", type="source")
	from multiprocessing.dummy import Pool
	from sklearn.cluster import KMeans
	import functools


	...

	pool = Pool()
	results = pool.map(functools.partial(find_cluster, kmeans=kmeans, X=X), clusters)
	Method 1: Using Docker file
	EXPOSE 5000-9999
	Method 2: Using Run commands
	docker run –expose=5000-9999
	or
	docker run -p 5000-9999:5000-9999
	reference: https://stackoverflow.com/questions/879432/what-is-the-difference-between-a-generative-and-discriminative-algorithm (by benhamner)

	Let's say you have input data x and you want to classify the data into labels y. A generative model learns the joint probability distribution p(x,y) and a discriminative model learns the conditional probability distribution p(y\|x) - which you should read as "the probability of y given x".

	Here's a really simple example. Suppose you have the following data in the form (x,y):

	(1,0), (1,0), (2,0), (2, 1)

	p(x,y) is