I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
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## Function to create an n-dimensional array | |
# Main function to call | |
# typeOfitem | |
# Should be a class which can generate objects | |
# e.g. float, int, complex, or any other type, such as | |
# myCoolClass | |
# | |
# dimensions | |
# value for a 1D array, or a list or tuple defining the |
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// Just before switching jobs: | |
// Add one of these. | |
// Preferably into the same commit where you do a large merge. | |
// | |
// This started as a tweet with a joke of "C++ pro-tip: #define private public", | |
// and then it quickly escalated into more and more evil suggestions. | |
// I've tried to capture interesting suggestions here. | |
// | |
// Contributors: @r2d2rigo, @joeldevahl, @msinilo, @_Humus_, | |
// @YuriyODonnell, @rygorous, @cmuratori, @mike_acton, @grumpygiant, |
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build/ |
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def is_port_open?(ip, port) | |
begin | |
Timeout::timeout(20) do | |
begin | |
s = TCPSocket.new(ip, port) | |
s.close | |
return true | |
rescue Errno::ECONNREFUSED, Errno::EHOSTUNREACH | |
return false | |
end |
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""" From: http://danielhnyk.cz/predicting-sequences-vectors-keras-using-rnn-lstm/ """ | |
from keras.models import Sequential | |
from keras.layers.core import TimeDistributedDense, Activation, Dropout | |
from keras.layers.recurrent import GRU | |
import numpy as np | |
def _load_data(data, steps = 40): | |
docX, docY = [], [] | |
for i in range(0, data.shape[0]/steps-1): | |
docX.append(data[i*steps:(i+1)*steps,:]) |
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# <type>: (If applied, this commit will...) <subject> (Max 50 char) | |
# |<---- Using a Maximum Of 50 Characters ---->| | |
# Explain why this change is being made | |
# |<---- Try To Limit Each Line to a Maximum Of 72 Characters ---->| | |
# Provide links or keys to any relevant tickets, articles or other resources | |
# Example: Github issue #23 |
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@echo off | |
powershell -NoProfile -ExecutionPolicy ByPass -File "%~d0%~p0%~n0.ps1" |
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#!/usr/bin/env python | |
from collections import defaultdict | |
from pprint import pprint | |
import random | |
from pulp import * | |
def generate_trips(route_names, start_hour, end_hour): |