tomshanley

license: mit

krtx

#!/bin/bash

# tmux requires unrecognized OSC sequences to be wrapped with DCS tmux;
# <sequence> ST, and for all ESCs in <sequence> to be replaced with ESC ESC. It
# only accepts ESC backslash for ST.
function print_osc() {
    if [[ -n $TERM ]] ; then
        printf "\033Ptmux;\033\033]"
    else
        printf "\033]"

stevenworthington

# ipak function: install and load multiple R packages.
# check to see if packages are installed. Install them if they are not, then load them into the R session.

ipak <- function(pkg){
    new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
    if (length(new.pkg)) 
        install.packages(new.pkg, dependencies = TRUE)
    sapply(pkg, require, character.only = TRUE)
}

nioto

#!/usr/bin/python
'''
  A Simple mjpg stream http server for the Raspberry Pi Camera
  inspired by https://gist.github.com/n3wtron/4624820
'''
from BaseHTTPServer import BaseHTTPRequestHandler,HTTPServer
import io
import time
import picamera

Nurdok

Python Number Conversion Chart

From	To	Expression

dave-andersen

import tensorflow as tf
import numpy as np
import time

N=10000
K=4
MAX_ITERS = 1000

start = time.time()

waltarix

diff --git a/screen-write.c b/screen-write.c
index 15f8d07..8a175a6 100644
--- a/screen-write.c
+++ b/screen-write.c
@@ -1334,6 +1334,7 @@ screen_write_cell(struct screen_write_ctx *ctx, const struct grid_cell *gc)
 	ctx->cells++;
 
 	/* If the width is zero, combine onto the previous character. */
+	/*
 	if (width == 0) {

kevindavenport

haje01

도커와 AWS를 활용한 클라우드 딥러닝 환경 구축

글쓴이: 김정주(haje01@gmail.com)

최근 딥러닝 관련 패키지들은 대부분 CPU와 GPU를 함께 지원하고 있습니다. GPU를 사용하면 보다 빠르게 학습 결과를 낼 수 있지만, GPU를 활용하기 위해서는 NVIDIA계열의 그래픽 카드, 드라이버 S/W 그리고 CUDA의 설치를 필요로 합니다.

이 글에서는 AWS의 GPU 인스턴스와 도커를 활용해 딥러닝 패키지(Caffe)를 편리하게 사용하는 방법을 소개합니다.

---

mbostock

license: gpl-3.0
redirect: https://observablehq.com/@d3/multi-line-chart