chaser92

# BEFORE STARTING PUT libcudnn5_5.1.10-1+cuda8.0_amd64.deb AND libcudnn5-dev_5.1.10-1+cuda8.0_amd64.deb IN /tmp
# OTHERWISE THIS WON'T WORK
# This has been tested only on EC2 P2 xlarge instance with 16 GB storage and stock Ubuntu 16.04
# It's a setup for a playground EC2 machine to perform workshops with Jupyter Notebook on GPU.
# It might contain some unnecessary crap
# The process takes like 15 minutes

pushd /tmp &&
stat libcudnn5_5.1.10-1+cuda8.0_amd64.deb &&
stat libcudnn5-dev_5.1.10-1+cuda8.0_amd64.deb &&

sstevan

import ssl

from socks import create_connection
from socks import PROXY_TYPE_SOCKS4
from socks import PROXY_TYPE_SOCKS5
from socks import PROXY_TYPE_HTTP

from imaplib import IMAP4
from imaplib import IMAP4_PORT
from imaplib import IMAP4_SSL_PORT

bluekvirus

How To Serve Flask Applications with uWSGI and Nginx on Ubuntu 14.04

@credit Yan Zhu (https://github.com/nina-zhu)

Introduction

Flask is a microframework for Python based on Werkzeug, Jinja 2 and good intentions, it can help you get your Python application or website off the ground. Flask includes a simplified development server for testing your code locally, but for anything even slightly production related, a more secure and powerful web server is required.

In this guide, we will demonstrate how to install and configure some components on Ubuntu 14.04 to support and serve Flask applications. We will configure the uWSGI application container server to interface with our applications. We will then set up Nginx to reverse proxy to uWSGI, giving us access to its security and performance features to serve our apps.

Prerequisites and Goals

Ryanb58

# WOrks in python 2.7 not sure if it works in python 3.

# Just straight up connect by any means possible.
from ftplib import FTP_TLS

def connect():
    ftp = FTP_TLS()
    ftp.debugging = 2
    ftp.connect('localhost', 2121)
    ftp.login('developer', 'password')

offchan42

ATTENTION PLEASE

This gist has been moved to https://github.com/offchan42/machine-learning-curriculum

Please see that repository instead because you can make pull requests there and later updates will be pushed there too.

===

Machine Learning Curriculum

TomAugspurger

bearfrieze

Comprehensions in Python the Jedi way

by Bjørn Friese

Beautiful is better than ugly. Explicit is better than implicit.

-- The Zen of Python

I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.

ericjang

TensorFlow development environment on Windows using Docker

Here are instructions to set up TensorFlow dev environment on Docker if you are running Windows, and configure it so that you can access Jupyter Notebook from within the VM + edit files in your text editor of choice on your Windows machine.

Installation

First, install https://www.docker.com/docker-toolbox

Since this is Windows, creating the Docker group "docker" is not necessary.

msund

{
 "metadata": {
  "name": "Three new matplotlib plots"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

codekk

#!/usr/bin/env perl
#
# http://daringfireball.net/2007/03/javascript_bookmarklet_builder

use strict;
use warnings;
use URI::Escape qw(uri_escape_utf8);
use open  IO  => ":utf8",		# UTF8 by default
		  ":std";				# Apply to STDIN/STDOUT/STDERR