technoweenie

machine github.com
login technoweenie
password SECRET

machine api.github.com
login technoweenie
password SECRET

dAnjou

<VirtualHost *>
    ServerName example.com

    WSGIDaemonProcess www user=max group=max threads=5
    WSGIScriptAlias / /home/max/Projekte/flask-upload/flask-upload.wsgi

    <Directory /home/max/Projekte/flask-upload>
        WSGIProcessGroup www
        WSGIApplicationGroup %{GLOBAL}
        Order deny,allow

agnoster

agnoster.zsh-theme

A ZSH theme optimized for people who use:

• Solarized
• Git
• Unicode-compatible fonts and terminals (I use iTerm2 + Menlo)

For Mac users, I highly recommend iTerm 2 + Solarized Dark

SachaEpskamp

library("shiny")
library("foreign")

PoisonAlien

__author__ = "Anand M"

'''
Takes output file generated by VarScan2 somatic programme and converts the formats.
'''

import argparse, math, re

parser = argparse.ArgumentParser(
    description="Converts VarScan2 somatic vcf to native format and vice-versa.\nInput is automatically detected")

erikbern

# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

ipbastola

Safest way to clean up boot partition - Ubuntu 14.04LTS-x64, Ubuntu 16.04LTS-x64

Reference

Case I: if /boot is not 100% full and apt is working

1. Check the current kernel version

$ uname -r 

ivanleoncz

#/usr/bin/python3
""" Demonstration of logging feature for a Flask App. """

from logging.handlers import RotatingFileHandler
from flask import Flask, request, jsonify
from time import strftime

__author__ = "@ivanleoncz"

import logging

tokestermw

from __future__ import print_function

from keras import backend as K
from keras.engine import Input, Model, InputSpec
from keras.layers import Dense, Activation, Dropout, Lambda
from keras.layers import Embedding, LSTM
from keras.optimizers import Adam
from keras.preprocessing import sequence
from keras.utils.data_utils import get_file
from keras.datasets import imdb

jjallaire

library(keras)
library(reticulate)

layer_multiplicative_lstm <-function(
  object, units, activation = "tanh", recurrent_activation = "hard_sigmoid", use_bias = TRUE, 
  return_sequences = FALSE, return_state = FALSE, go_backwards = FALSE, stateful = FALSE, unroll = FALSE,
  kernel_initializer = "glorot_uniform", recurrent_initializer = "orthogonal", bias_initializer = "zeros", 
  unit_forget_bias = TRUE, kernel_regularizer = NULL, recurrent_regularizer = NULL, bias_regularizer = NULL,