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<script src="https://unpkg.com/jupyter-js-widgets@~2.1.4/dist/embed.js"></script>
<script type="application/vnd.jupyter.widget-state+json">
{
    "version_major": 1,
    "version_minor": 0,
    "state": {
        "7da35340b651476ba3926aa15366e2d8": {
            "model_name": "LayoutModel",
            "model_module": "jupyter-js-widgets",
            "model_module_version": "~2.1.4",

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#!/bin/bash

if [ "$1" = "-h" -o "$1" = "--help" -o -z "$1" ]; then cat <<EOF
appify v3.0.1 for Mac OS X - http://mths.be/appify
Creates the simplest possible Mac app from a shell script.

Appify takes a shell script as its first argument:

    `basename "$0"` my-script.sh

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##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

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Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

###Array ####Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.

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# gap.py
# (c) 2013 Mikael Vejdemo-Johansson
# BSD License
#
# SciPy function to compute the gap statistic for evaluating k-means clustering.
# Gap statistic defined in
# Tibshirani, Walther, Hastie:
#  Estimating the number of clusters in a data set via the gap statistic
#  J. R. Statist. Soc. B (2001) 63, Part 2, pp 411-423

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library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
"http://academic.udayton.edu/kissock/http/Weather/gsod95-current/NYNEWYOR.txt" %>%
	read.table() %>% data.frame %>% tbl_df -> data
names(data) <- c("month", "day", "year", "temp")

data %>%
	group_by(year, month) %>%

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# Call virtualenvwrapper's "workon" if .venv exists.  This is modified from--
# http://justinlilly.com/python/virtualenv_wrapper_helper.html
# which is linked from--
# http://virtualenvwrapper.readthedocs.org/en/latest/tips.html#automatically-run-workon-when-entering-a-directory
check_virtualenv() {
    if [ -e .venv ]; then
        env=`cat .venv`
        if [ "$env" != "${VIRTUAL_ENV##*/}" ]; then
            echo "Found .venv in directory. Calling: workon ${env}"
            workon $env

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import multiprocessing
from StringIO import StringIO
import gzip
import csv
from random import shuffle
import numpy as np
import json
from time import sleep
import pymongo
import datetime

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import time
import random
import csv
import pandas as pd
import pickle
import random
import datetime
import os
import re
from itertools import izip_longest

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import time
from selenium import webdriver
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.keys import Keys
import random
import csv
import pandas as pd