Manuel Romero mrm8488

## from_kaggle_to_colab.py
# Easy Steps to persist Kaggle profile by @mrm8488 (Manuel Romero)

# Download kaggle.json from Kaggle -- MyAccount -> Create New API Token -> auto downloads as "kaggle.json

# Import json into notebook - run in a cell

from google.colab import files
files.upload()

# Browse to downloaded kaggle.json and upload

## express-cache.js
'use strict'

var express = require('express');
var app = express();
var mcache = require('memory-cache');

app.set('view engine', 'jade');

var cache = (duration) => {
  return (req, res, next) => {

## train_swarm.sh
#!/bin/sh
set -x

# == Swarm training (alpha release) ==

# Setup:
#
#   git clone https://github.com/shawwn/gpt-2
#   cd gpt-2
#   git checkout dev-shard

## pdfToMP3.py
import pdftotext
from gtts import gTTS
from sys import argv
with open(argv[1], "rb") as f:
    pdf = pdftotext.PDF(f)
    document= "\n\n".join(pdf)
    tts = gTTS(document)
    print("Saving Audio file")
    tts.save(argv[1]+".mp3")

## create_social_card.js
export default function generateSocialImage({
    title,
    tagline,
+   cloudName,
+   imagePublicID,
+   cloudinaryUrlBase = 'https://res.cloudinary.com',
+   version = null,
    titleFont = 'arial',
    titleExtraConfig = '',
    taglineExtraConfig = '',

## Info-commands.sh
printenv

ifconfig -a

iptable -L

cat /etc/apache2/site-enabled/*

netstat -punta

## vector_matrix.js
const array = (size,value = 0) => Array(size).fill(value);
let a = array(n);

const matrix = (rows,cols,value = 0) => Array(rows).fill(Array(cols).fill(value));
let m = matrix(r,c);

## an-inquiry-into-matplotlib-figures.ipynb

      
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                mrm8488
                / an-inquiry-into-matplotlib-figures.ipynb
            
            
              Created
              December 23, 2019 20:55
                — forked from akashpalrecha/an-inquiry-into-matplotlib-figures.ipynb
            
              
                This notebook dives deep into Matplotlib's Figures, Axes, subplots and the very amazing GridSpec!
              
          
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## matrix_effect.html
<html>

<head>
    <style>
        body {
            margin: 0;
            height: 100vh;
            width: 100vw;
        }
    </style>

## nodejs_readline.js

// Node.js readline & async iteration:
const fs = require('fs");
const {createlnterface: ci} =
require('readline’);
async function logLines(ls) {

   for await (const l of ls) {

      console.log('>"' + l);
	# Easy Steps to persist Kaggle profile by @mrm8488 (Manuel Romero)

	# Download kaggle.json from Kaggle -- MyAccount -> Create New API Token -> auto downloads as "kaggle.json

	# Import json into notebook - run in a cell

	from google.colab import files
	files.upload()

	# Browse to downloaded kaggle.json and upload
	'use strict'

	var express = require('express');
	var app = express();
	var mcache = require('memory-cache');

	app.set('view engine', 'jade');

	var cache = (duration) => {
	return (req, res, next) => {
	#!/bin/sh
	set -x

	# == Swarm training (alpha release) ==

	# Setup:
	#
	# git clone https://github.com/shawwn/gpt-2
	# cd gpt-2
	# git checkout dev-shard
	import pdftotext
	from gtts import gTTS
	from sys import argv
	with open(argv[1], "rb") as f:
	pdf = pdftotext.PDF(f)
	document= "\n\n".join(pdf)
	tts = gTTS(document)
	print("Saving Audio file")
	tts.save(argv[1]+".mp3")
	export default function generateSocialImage({
	title,
	tagline,
	+ cloudName,
	+ imagePublicID,
	+ cloudinaryUrlBase = 'https://res.cloudinary.com',
	+ version = null,
	titleFont = 'arial',
	titleExtraConfig = '',
	taglineExtraConfig = '',
	printenv

	ifconfig -a

	iptable -L

	cat /etc/apache2/site-enabled/*

	netstat -punta
	const array = (size,value = 0) => Array(size).fill(value);
	let a = array(n);

	const matrix = (rows,cols,value = 0) => Array(rows).fill(Array(cols).fill(value));
	let m = matrix(r,c);
	<html>

	<head>
	<style>
	body {
	margin: 0;
	height: 100vh;
	width: 100vw;
	}
	</style>

	// Node.js readline & async iteration:
	const fs = require('fs");
	const {createlnterface: ci} =
	require('readline’);
	async function logLines(ls) {

	for await (const l of ls) {

	console.log('>"' + l);