vgoklani

# dropping a database via pymongo
from pymongo import Connection
c = Connection()
c.drop_database('mydatabase')

# drop a collection via pymongo
from pymongo import Connection
c = Connection()
c['mydatabase'].drop_collection('mycollection')

joaopizani

# the following two lines give a two-line status, with the current window highlighted
hardstatus alwayslastline
hardstatus string '%{= kG}[%{G}%H%? %1`%?%{g}][%= %{= kw}%-w%{+b yk} %n*%t%?(%u)%? %{-}%+w %=%{g}][%{B}%m/%d %{W}%C%A%{g}]'

# huge scrollback buffer
defscrollback 5000

# no welcome message
startup_message off

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

SavvyGuard

import boto
import boto.s3

import os.path
import sys

# Fill these in - you get them when you sign up for S3
AWS_ACCESS_KEY_ID = ''
AWS_ACCESS_KEY_SECRET = ''
# Fill in info on data to upload

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

joelouismarino

from __future__ import print_function
import imageio
from PIL import Image
import numpy as np
import keras

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

ameasure

from keras.engine.topology import Layer
from keras import initializations
from keras import backend as K

class Attention(Layer):
    '''Attention operation for temporal data.
    # Input shape
        3D tensor with shape: `(samples, steps, features)`.
    # Output shape
        2D tensor with shape: `(samples, features)`.

cbaziotis

from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer


def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:

nicor88

#!/usr/bin/env bash
set -x -e

JUPYTER_PASSWORD=${1:-"myJupyterPassword"}
NOTEBOOK_DIR=${2:-"s3://myS3Bucket/notebooks/"}

# home backup
if [ ! -d /mnt/home_backup ]; then
  sudo mkdir /mnt/home_backup
  sudo cp -a /home/* /mnt/home_backup