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#!/bin/bash

sudo kextunload -b com.apple.iokit.BroadcomBluetoothHostControllerUSBTransport
sudo kextload -b com.apple.iokit.BroadcomBluetoothHostControllerUSBTransport

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docker run -i -t --name nodejs ubuntu:latest /bin/bash

So here, -i stands for interactive mode and -t will allocate a pseudo terminal for us.

Some more trivia about these flags.

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Install iTerm 2

Download, unzip and drag to your Applications directory.

https://www.iterm2.com/downloads.html

Install Fish

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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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// This works on all devices/browsers, and uses IndexedDBShim as a final fallback 
var indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB || window.shimIndexedDB;

// Open (or create) the database
var open = indexedDB.open("MyDatabase", 1);

// Create the schema
open.onupgradeneeded = function() {
    var db = open.result;
    var store = db.createObjectStore("MyObjectStore", {keyPath: "id"});

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import shutil
import tempfile

from django.conf import settings
from django.core.files.storage import FileSystemStorage
from django.db.models import FileField
from django.apps.apps import get_model, get_models
from django.test.runner import DiscoverRunner

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import h5py

f = h5py.File('path/to/file', 'r')

# List all groups
print('Keys: %s' % f.keys())

# Get data
key = 'random_data'
random_data = np.asarray(f[key])

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import pstats, cProfile

def recip_square(i):
    return 1./i**2

def approx_pi(n=10000000):
    val = 0.
    for k in range(1,n+1):
        val += recip_square(k)
    return (6 * val)**.5

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import cython
cimport cython
import numpy as np
cimport numpy as np

cdef extern from "utils.h":
  void print_array(double *, int)
  
def print_np_as_c(np.ndarray[double, ndim=2, mode="c"] arr not None):
  cdef int n

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from keras.models import Model
from keras.applications.vgg16 import VGG16
from keras.layers import GlobalAveragePooling2D

# Wait for downloading for the 1st time,
# the weights is saved in ~/.keras/models
vgg16 = VGG16(include_top=False,  # don't include 3 fully connected layers
              weights='imagenet', # use pre-trained weights, not random initialization
              pooling=None)       # don't apply any pooling at the last convolutional layer