swayson

import pandas as pd
from github import Github

g = Github("username", "password")

final = ({'url':r.html_url , 'name': r.name} for r in g.get_user().get_starred())
pd.DataFrame(final).to_excel('Github Stars 20160101.xlsx')

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##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

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# DEFINE ENVIRONMENT VARIABLES

apt-get update 
apt-get upgrade -y 


# INSTALL PACKAGES
apt-get install -y aria2

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##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

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ONE_SENTENCE_DESCRIPTION
PARAGRAPH_DESCRIPTION
Parameters
----------
PARAM_NAME : DTYPE
              DESCRIBE PARAMETER
... 
Returns
-------
RETURN_NAME : DTYPE

swayson

SELECT *  FROM table
  WHERE _ROWID_ >= (abs(random()) % (SELECT max(_ROWID_) FROM table))
LIMIT 1

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#!/usr/bin/env bash
#Code adapted from https://gist.github.com/yangj1e/3641843c758201ebbc6c (Modified to Python3.5)

cd ~
#wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda2-2.4.0-Linux-x86_64.sh
wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda3-2.4.1-Linux-x86_64.sh
bash Anaconda3-2.4.1-Linux-x86_64.sh -b
echo 'PATH="/home/ubuntu/anaconda3/bin:$PATH"' >> .bashrc
. .bashrc

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# Phase 1 - Preparation:
Acquire data
Reformat and clean data

# Phase 2 - Analysis:
Edit analysis scripts
Execute analysis scripts
Inspect outputs
Debug

swayson

# Phase 1 - Preparation:
Acquire data
Reformat and clean data

# Phase 2 - Analysis:
Edit analysis scripts
Execute analysis scripts
Inspect outputs
Debug

swayson

import logging


def log_event(func):
    """
    """
    def wrapper(*args, **kwargs):

        # Code before function call
        # <ADD IT HERE>