ThriceGood

import logging
import os

class Logger:

	def __init__(self, name):
		self.create_log_dir()
		self.logger = logging.getLogger('{}'.format(name))
		format = "%(asctime)s [%(levelname)s]:  %(message)s"
		logging.basicConfig(format=format, level=logging.DEBUG)

dmyersturnbull

import pandas as pd
import itertools
import time
import multiprocessing
from typing import Callable, Tuple, Union

def groupby_parallel(
  groupby_df: pd.core.groupby.DataFrameGroupBy,
  func: Callable[[Tuple[str, pd.DataFrame]], Union[pd.DataFrame, pd.Series]],
  num_cpus: int = multiprocessing.cpu_count() - 1,

aabadie

"""Persistence strategies comparison script.

This script compute the speed, memory used and disk space used when dumping and
loading arbitrary data. The data are taken among:
- scikit-learn Labeled Faces in the Wild dataset (LFW)
- a fully random numpy array with 10000x10000 shape
- a dictionary with 1M random keys/values
- a list containing 10M random value
The compared persistence strategies are:

robdmc

#! /usr/bin/env python
import logging
import datetime
import fleming
import pytz
import os
from itertools import islice
import sys

shagunsodhani

The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.

Issues With Training Deep Neural Networks

Internal Covariate shift

Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.

It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.

fredbenenson

layout	title	description	tags
default	SQL Style Guide	A guide to writing clean, clear, and consistent SQL.	data process

Purpose

mbejda

domain,name,keywords,description,twitter
walmartstores.com,Wal-Mart Stores,"SEC filing,Walmart photos,walmart stock,sustainability index,Wal-Mart,walmart annual reports,Walmart responsibility,executive speeches,walmart suppliers,global responsibility,walmart global sustainability report,walmart investors,Walmart interactive map,walmart history,privacy policy,financial reports,walmart news,Wal Mart,walmart sustainability,Walmart locations,Walmart videos,walmart story,Walmart,Walmart stores,walmart board of directors,community giving,walmart careers,Walmart jobs,sam walton","Find Walmart executive speeches, financial reports, press releases, downloadable photos and videos, and see an interactive map of our locations around the world.",walmart
gm.com,General Motors,,"General Motors is home to Buick, Cadillac, GMC and Chevrolet. Find the latest news about GM automotive innovations, investor relations and more. ",GM
ge.com,General Electric,,,generalelectric
chevron.com,ChevronTexaco,"cvx, chevrontexaco, cheveron, 

phobson

GaelVaroquaux

'''
Non-parametric computation of entropy and mutual-information

Adapted by G Varoquaux for code created by R Brette, itself
from several papers (see in the code).

This code is maintained at https://github.com/mutualinfo/mutual_info 
Please download the latest code there, to have improvements and 
bug fixes.

ulope

# Written by Brendan O'Connor, [email protected], www.anyall.org
#  * Originally written Aug. 2005
#  * Posted to gist.github.com/16173 on Oct. 2008

#   Copyright (c) 2003-2006 Open Source Applications Foundation
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
#