Tiberiu Ichim tiberiuichim

This worked for me and might not work for your.

Try it at your own risk!

Add the neon repositories and install the neon desktop

$ wget -qO - 'http://archive.neon.kde.org/public.key' | sudo apt-key add -
$ sudo apt-add-repository http://archive.neon.kde.org/user
$ sudo apt-get update
$ sudo apt-get install neon-desktop

Awesome NLP on AWS GPU

Tech:

Ubuntu
Nvidia Cuda
Python
Theano
TensorFlow
Keras

What forces layout / reflow

All of the below properties or methods, when requested/called in JavaScript, will trigger the browser to synchronously calculate the style and layout*. This is also called reflow or layout thrashing, and is common performance bottleneck.

Generally, all APIs that synchronously provide layout metrics will trigger forced reflow / layout. Read on for additional cases and details.

Element APIs

Getting box metrics

elem.offsetLeft, elem.offsetTop, elem.offsetWidth, elem.offsetHeight, elem.offsetParent

Hello, visitors! If you want an updated version of this styleguide in repo form with tons of real-life examples… check out Trellisheets! https://github.com/trello/trellisheets

Trello CSS Guide

“I perfectly understand our CSS. I never have any issues with cascading rules. I never have to use !important or inline styles. Even though somebody else wrote this bit of CSS, I know exactly how it works and how to extend it. Fixes are easy! I have a hard time breaking our CSS. I know exactly where to put new CSS. We use all of our CSS and it’s pretty small overall. When I delete a template, I know the exact corresponding CSS file and I can delete it all at once. Nothing gets left behind.”

You often hear updog saying stuff like this. Who’s updog? Not much, who is up with you?

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	from datetime import datetime

	import string

	from nltk.stem.lancaster import LancasterStemmer
	from nltk.corpus import stopwords

	#Gets the tweet time.
	def get_time(tweet):
	return datetime.strptime(tweet['created_at'], "%a %b %d %H:%M:%S +0000 %Y")

	import json
	import urlparse
	from itertools import chain
	flatten = chain.from_iterable

	from nltk import word_tokenize

	from gensim.corpora import Dictionary
	from gensim.models.ldamodel import LdaModel
	from gensim.models.tfidfmodel import TfidfModel