So-Cool

from pprint import pprint
import jinja2
import markdown

HTML_TEMPLATE = """{% macro get_html() %}
{{ content | markdown }}
{% endmacro %}
{% set html_content = get_html() %}

Title from Markdown meta-data: {{ get_title() }}

So-Cool

So-Cool

So-Cool

Automated story comprehension

This notebook shows how to extract background knowledge and relations from a story text. To this end, we use Stanford's NLP parser available to download here.

We parsed the following story (story.txt file):

Mary was sleeping.
Her phone rang.
She was annoyed.
Mary answered the phone.
Ann told the good news to Mary.

So-Cool

Perfect10

How it works?

How to find a 'perfect 10'?

This simple algorithm applies Optional Stopping Time Theorem used for calculating optimal exercising time for American Options. Simply, it tells you whether your current partner is optimal.
Optimality is calculated in a sens of score represented by integer: 1--10, that you assign to your current partner. You also need to specify a few more parameters like:

• score(1--10) of you current partner,

So-Cool

This gist includes simple Cuckoo malware reports dataset used for Cuckoo Machine Learning project.

So-Cool

So-Cool

So-Cool

So-Cool

import os
import sys
from bs4 import BeautifulSoup

if len(sys.argv) != 3:
    sys.exit("First argument is HTML second is an output directory.")

with open(sys.argv[1], "r") as html:
    soup = BeautifulSoup(html.read(), "html.parser")