MLnick

import sys

from pyspark.context import SparkContext
from numpy import array, random as np_random
from sklearn import linear_model as lm
from sklearn.base import copy

N = 10000   # Number of data points
D = 10      # Numer of dimensions
ITERATIONS = 5

FZambia

from __future__ import print_function
from tornado.gen import Task, Return, coroutine
import tornado.process
from tornado.ioloop import IOLoop
import subprocess
import time


STREAM = tornado.process.Subprocess.STREAM

williamstein

###############################################################################
# Copyright (c) 2013, William Stein
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
#    list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice,

chengyin

// 1. Go to page https://www.linkedin.com/settings/email-frequency
// 2. You may need to login 
// 3. Open JS console
//    ([How to?](http://webmasters.stackexchange.com/questions/8525/how-to-open-the-javascript-console-in-different-browsers))
// 4. Copy the following code in and execute
// 5. No more emails
//
// Bookmarklet version: 
// http://chengyin.github.io/linkedin-unsubscribed/

wsargent

303 See Other

Location: https://github.com/wsargent/docker-cheat-sheet

Hi there!

The docker cheat sheet has moved to a Github project under https://github.com/wsargent/docker-cheat-sheet.

Please click on the link above to go to the cheat sheet.

syhw

"""
A deep neural network with or w/o dropout in one file.

License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
"""

import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams

schaunwheeler

from numpy.random import RandomState
import pyspark.sql.functions as f
from pyspark import StorageLevel


def hashmin_jaccard_spark(
    sdf, node_col, edge_basis_col, suffixes=('A', 'B'), 
    n_draws=100, storage_level=None, seed=42, verbose=False):
    """
    Calculate a sparse Jaccard similarity matrix using MinHash.

dive

Grammarly iOS Keyboard – Unboxing

Grammarly is a lovely writing assistant. Twenty million people use the tool across the globe according to the statistics available on the site. I use it too. No complaints, it does its job quite well, and I am a happy user. The only thing that always worried me is the Grammarly iOS Keyboard. As an iOS engineer, I know how easily you can collect different data, sensitive information, and even do not ask users about the consent. So, let's check what is inside the Grammarly iOS application.

TL;DR

• Grammarly iOS uses at least three trackers (Adjust, AppsFlyer, Internal analytics)
• Grammarly Keyboard has access to keystrokes, sensitive data, and able to send them over the network

Frameworks