ChanYub Park mrchypark

Docker 치트 시트

한국어 번역(초벌): nacyot
같이 읽으면 좋은 문서들

왜 Docker를 사용해야하는가?

osmc 세팅

빠른 세팅을 위한 개인 설정 기록
osmc rc3, raspberry pi 2 기준

한글화

스킨 한글화

폰트 설치

sudo apt-get install fonts-nanum
sudo cp /usr/share/fonts/truetype/nanum/NanumGothic.ttf /usr/share/kodi/addons/skin.osmc/fonts/

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
How does PyTorch cwrap work to generate code for Tensor methods?
How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.


	#########################################
	## GLOBAL REQUIREMENTS AND DEFINITIONS ##
	#########################################
	require(RCurl)
	require(XML)

	loginURL <- "https://accounts.google.com/ServiceLogin"
	authenticateURL <- "https://accounts.google.com/accounts/ServiceLoginAuth"
	insightsURL <- "http://www.google.com/insights/search/overviewReport"

	0:
	messages:
	- Hello!
	- I'm a Bot!
	next: 1

	1:
	messages:
	- "What's your name?"
	input:

	0:
	messages:
	- "Would you like to send me a picture?"
	input:
	type: buttons_single_select
	key: picture_yes_no
	options:
	[Yes, sure]:
	input:
	type: image

	library(shiny)
	library(gsheet)

	# Helper Functions
	read.list <- function(url = "https://docs.google.com/spreadsheets/d/1aJ2Bv8CCR4OhBoVdsQD16OWON89VwuaLYKDDP-OiTG4") {
	as.data.frame(gsheet::gsheet2tbl(url))
	}

	read.data <- function(url) {
	data <- as.data.frame(gsheet::gsheet2tbl(url))

	# Installing TOR on mac: brew install tor
	# Run TOR on custom port: tor --SOCKSPort 9050

	# Check the 'origin' field in the response to verify TOR is working.
	library(httr)
	GET("https://httpbin.org/get", use_proxy("socks5://localhost:9050"))

	# Set proxy in curl
	library(curl)
	h <- new_handle(proxy = "socks5://localhost:9050")

	# coding: utf-8
	from __future__ import print_function
	import numpy as np
	from keras.models import Sequential
	from keras.layers import Embedding

	window_size = 1

	# using skipgram embeddings built using fasttext:
	# fasttext skipgram -input dataset -output dataset.skipgram