carpedm20

#-*- coding: utf-8 -*-
import json
import requests

from tqdm import tqdm


DIST = 0.01

HEADERS = {

carpedm20

NAF-tensorflow

First, clone the project:

git clone https://github.com/devsisters/NAF-tensorflow/
cd NAF-tensorflow

To train a model for Pendulum:

python main.py --env_name=Pendulum-v0 --is_train=True

carpedm20

DQN-tensorflow

First, clone the project:

git clone https://github.com/devsisters/DQN-tensorflow/
cd DQN-tensorflow

To train a model for Breakout:

python main.py --env_name=Breakout-v0 --is_train=True

carpedm20

import requests

r = requests.get("http://naver.com")
html = r.text

idx = html.find("<title>")
print html[idx:idx+20]

carpedm20

#!/usr/bin/python
import smtplib
from email.mime.text import MIMEText
import netrc
from docopt import docopt

DEFAULTFROM = "admin@minsky.unist.ac.kr"
DEFAULTTO = "carpedm20@gmail.com"
SMSADDRESS = "carpedm20@messaging.sprintpcs.com"
DEFAULTSUBJECT = "Job finished from PAIL Minsky"

carpedm20

Intercepts HTTPs Traffic with Python & mitmproxy

Introduction

Modern applications usually make use of back-end API servers to provide their services. With a non-transparent HTTPs proxy, which intercepts the communication between clients and servers (aka the man-in-the-middle scheme), you can easily manipulate both API requests and responses.

This manual helps you create your own proxy with Python and mitmproxy/libmproxy. Mitmproxy ships with both a standalone command-line tool (mitmproxy) and a Python library (libmproxy).

carpedm20

#!/bin/bash

PREFIX=/home/carpedm20/lib

## make !
{
  ## clean just incase
  make clean -C .

  ## build

carpedm20

#!/bin/sh
image_path=/home/carpedm20/imagenet
for name in $image_path/val/*.JPEG; do
  convert -resize 256x256\! $name $name
done

carpedm20

print 'aaa'*2+hex(0x20acecafe-0xdeadbeef-eval('0x'+'aaaa'.encode('hex'))*2)[2:].decode('hex')

carpedm20

#-*- coding: utf-8 -*-
import xml.etree.ElementTree as ET

tree = ET.parse('dict-ko-galkwi.xml')
root = tree.getroot()

texts = [i.text for i in root.iter('word')]
poses = [i.text for i in root.iter('pos')]

zipped_texts = zip(texts, poses)