viksit

git branch -m old_branch new_branch         # Rename branch locally    
git push origin :old_branch                 # Delete the old branch    
git push --set-upstream origin new_branch   # Push the new branch, set local branch to track the new remote

viksit

class ThreadPoolExecutor(concurrent.futures.ThreadPoolExecutor):
     """
     A version of :class:`concurrent.futures.ThreadPoolExecutor` that
     always uses native threads, even when threading is monkey-patched.

     .. versionadded:: 1.2a1
     """

     def __init__(self, max_workers):
         super(ThreadPoolExecutor, self).__init__(max_workers)

viksit

import numpy as np

__author__ = 'Fariz Rahman'


def eq(x, y):
	return x.lower().replace(" ", "") == y.lower().replace(" ", "")

def get_words(x):
	x = x.replace("  ", " ")

viksit

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

viksit

from time import sleep
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, asynchronous, RequestHandler
from multiprocessing.pool import ThreadPool

_workers = ThreadPool(10)

def run_background(func, callback, args=(), kwds={}):
    def _callback(result):

viksit

"""Asynchronous requests in Flask with gevent"""

from time import time

from flask import Flask, Response

from gevent.pywsgi import WSGIServer
from gevent import monkey

import requests

viksit

#!/usr/bin/env python
# see http://matpalm.com/blog/2015/03/28/theano_word_embeddings/
import theano
import theano.tensor as T
import numpy as np
import random

E = np.asarray(np.random.randn(6, 2), dtype='float32')
t_E = theano.shared(E)
t_idxs = T.ivector()

viksit

#!/usr/bin/python
# load glove
import codecs
import array
import collections
import io
try:
    # Python 2 compat
    import cPickle as pickle
except ImportError:

viksit

"""
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)

viksit

__author__ = 'Conan'

import numpy as np
import random
from keras.models import Sequential
from keras.layers.recurrent import SimpleRNN



# Toy dictionary of 1000 indices to random length 10 vectors