marcelcaraciolo

def sigmoid(X):
    '''Compute the sigmoid function '''
    #d = zeros(shape=(X.shape))

    den = 1.0 + e ** (-1.0 * X)

    d = 1.0 / den

    return d

tonious

/* Read this comment first: https://gist.github.com/tonious/1377667#gistcomment-2277101
 * 2017-12-05
 * 
 *  -- T.
 */

#define _XOPEN_SOURCE 500 /* Enable certain library functions (strdup) on linux.  See feature_test_macros(7) */

#include <stdlib.h>
#include <stdio.h>

yong27

import multiprocessing
import pandas as pd
import numpy as np

def _apply_df(args):
    df, func, kwargs = args
    return df.apply(func, **kwargs)

def apply_by_multiprocessing(df, func, **kwargs):
    workers = kwargs.pop('workers')

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.

bzhr

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [

decodebiology

echo `date`;
dir=(`dirname $1`)

base=(`basename $1`)



echo "1/7 Preparing files";
cat $1 | sed 's/ "/\t/g' | sed 's/"; /\t/g' | cut -f1,4,5,7,10,12,16,18,22 | sed 's/";//' | awk '!x[$6]++' | awk '{print $6"\t"$0;}' > $dir/enst_annotation.tmp;

tnachen

# Prepare Steps:
# We are assuming we’re using CUDA 7.5
# Clone and Build latest Mesos with GPUs (skipping instructions here…)
# CUDA prepare script for ubuntu 14.04 with AWS G2 instance. 
# http://tleyden.github.io/blog/2015/11/22/cuda-7-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/
# Start mesos master and slave
# ./mesos-master --work_dir=/tmp/mesos
# sudo GLOG_v=1 ./mesos-slave --isolation=gpu/nvidia --master=`hostname -i`:5050 --nvidia_gpu_devices=0

# Add cuda bin to PATH

doowon

#! /bin/sh
### BEGIN INIT INFO
# Provides:          jupyter
# Required-Start:    $remote_fs $syslog
# Required-Stop:     $remote_fs $syslog
# Default-Start:     2 3 4 5
# Default-Stop:      0 1 6
# Short-Description: Start jupyter
# Description:       This file should be used to construct scripts to be
#                    placed in /etc/init.d.

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm