This post reviews several methods for converting a Markdown (.md) formatted file to PDF, from UNIX or Linux machines.
$ pandoc How_I_got_svg-resizer_working_on_Mac_OSX.md -s -o test1.pdf
// No Security | |
{ | |
"rules": { | |
".read": true, | |
".write": true | |
} | |
} |
RECAPTCHA_PUBLIC_KEY = '<public key>' | |
RECAPTCHA_PRIVATE_KEY = '<private key>' | |
def checkRecaptcha(response, secretkey): | |
url = 'https://www.google.com/recaptcha/api/siteverify?' | |
url = url + 'secret=' + str(secretkey) | |
url = url + '&response=' +str(response) |
import pytesseract | |
import sys | |
import argparse | |
try: | |
import Image | |
except ImportError: | |
from PIL import Image | |
from subprocess import check_output | |
from lxml import html | |
import csv, os, json | |
import requests | |
from exceptions import ValueError | |
from time import sleep | |
def linkedin_companies_parser(url): | |
for i in range(5): | |
try: |
import React from 'react' | |
import axios, { post } from 'axios'; | |
class SimpleReactFileUpload extends React.Component { | |
constructor(props) { | |
super(props); | |
this.state ={ | |
file:null | |
} |
""" | |
Python script for batch geocoding of addresses using the Google Geocoding API. | |
This script allows for massive lists of addresses to be geocoded for free by pausing when the | |
geocoder hits the free rate limit set by Google (2500 per day). If you have an API key for paid | |
geocoding from Google, set it in the API key section. | |
Addresses for geocoding can be specified in a list of strings "addresses". In this script, addresses | |
come from a csv file with a column "Address". Adjust the code to your own requirements as needed. | |
After every 500 successul geocode operations, a temporary file with results is recorded in case of | |
script failure / loss of connection later. | |
Addresses and data are held in memory, so this script may need to be adjusted to process files line |
Here are the simple steps needed to create a deployment from your local GIT repository to a server based on this in-depth tutorial.
You are developing in a working-copy on your local machine, lets say on the master branch. Most of the time, people would push code to a remote server like github.com or gitlab.com and pull or export it to a production server. Or you use a service like deepl.io to act upon a Web-Hook that's triggered that service.
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |