rcgalbo

# download: remote -> local
scp user@remote_host:remote_file local_file 

# upload: local -> remote
scp local_file user@remote_host:remote_file

rcgalbo

$ brew install fish
$ echo "/usr/local/bin/fish" | sudo tee -a /etc/shells
$ chsh -s `which fish`

rcgalbo

import React, {Component} from 'react';

class someFeature extends Component {
  constructor(props){
    super(props);

    this.state = {};
  }

rcgalbo

meeting notes - COMPANY-NAME

contact: {CONTACT NAME}

metrics:

offering:

rcgalbo

FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04

LABEL com.nvidia.volumes.needed="nvidia_driver"

RUN echo "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list

RUN apt-get update && apt-get install -y --allow-downgrades --no-install-recommends \
         build-essential \
         cmake \
         git \

rcgalbo

var express = require('express');
var bodyParser = require('body-parser');
var app = express();
app.set('view engine', 'ejs');
app.use(bodyParser.urlencoded({ extended: false }))

app.listen(4000, function() {
  console.log('server is listening!!');
});

rcgalbo

Keybase proof

I hereby claim:

• I am rgalbo on github.
• I am rgalbo (https://keybase.io/rgalbo) on keybase.
• I have a public key ASDdh70Q-ZM9yDCT_uzsC1K6r2AhNk__d590rTRF-d5Pogo

To claim this, I am signing this object:

rcgalbo

ui<-fluidPage(DT::dataTableOutput("wideTable"))

server<-function(input, output) { 
  output$wideTable <- DT::renderDataTable({
    table <- do.call(cbind, lapply(1:100, function(i) 1:1000))
    colnames(table) <- paste0("name.", 1:100)
    DT::datatable(table, options = list(scrollX = TRUE))
  })
}

rcgalbo

import MySQLdb

# Open database connection ( If database is not created don't give dbname)
db = MySQLdb.connect("localhost","yourusername","yourpassword","yourdbname" )

# prepare a cursor object using cursor() method
cursor = db.cursor()

# For creating create db
# Below line  is hide your warning 

rcgalbo

{
    "image_data_format": "channels_last",
    "epsilon": 1e-07,
    "floatx": "float32",
    "backend": "tensorflow"
}