chitchcock

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

chapmanb

#!/usr/local/bin/python
# Copyright (C) 2004 Rune Linding & Lars Juhl Jensen - EMBL
# The DisEMBL is licensed under the GPL license
# (http://www.opensource.org/licenses/gpl-license.php)
# DisEMBL pipeline

from string import *
from sys import argv
from Bio import SeqIO
import fpformat

arq5x

#!/usr/bin/env python
import sys
import pprint

def make_grantham_dict(grantham_mat_file):

        """
	Citation:   http://www.ncbi.nlm.nih.gov/pubmed/4843792
	Provenance: http://www.genome.jp/dbget-bin/www_bget?aaindex:GRAR740104

larsmans

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics.pairwise import rbf_kernel


class KMeansTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, centroids):
        self.centroids = centroids

    def fit(self, X, y=None):
        return self

kiyukuta

#coding: utf8
"""
1. Download this gist.
2. Get the MNIST data. 
    wget http://deeplearning.net/data/mnist/mnist.pkl.gz 
3. Run this code.
    python autoencoder.py 100 -e 1 -b 20 -v 
"""
import numpy
import argparse

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

avrilcoghlan

import sys
import os
from collections import defaultdict
import calc_dists_to_top_of_GO
import calc_dists_to_top_of_GO_using_bfs

class Error (Exception): pass
      
#====================================================================#

ameyavilankar

import string
import nltk
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
import re

def preprocess(sentence):
	sentence = sentence.lower()
	tokenizer = RegexpTokenizer(r'\w+')
	tokens = tokenizer.tokenize(sentence)

vals

syhw

"""
A deep neural network with or w/o dropout in one file.
"""

import numpy
import theano
import sys
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams