rcdilorenzo

const puppeteer = require('puppeteer');
const read = require('read');
const htmlToText = require('html-to-text').fromString;
const R = require('ramda');
const Promise = require('bluebird');
const fs = require('fs');
const download = require('@jinphen/download2');
const { CookieJar } = require('tough-cookie');

const mapSeries = R.flip(Promise.mapSeries);

abalter

• Install EVERYTHING you can with conda
• Install things you can with:
  • install.packages(, lib="")
  • install.github(, lib="")
  • biocLite(, lib="")
• Make separate local libs for different R versions
• After installing a package to a local lib, look there and see if there are dependencies you could have installed with conda.
• Or, head it off by looking at the dependencies in CRAN or Bioconductor

talegari

###############################################################################
#
# cor2
# -- Compute correlations of columns of a dataframe of mixed types
#
###############################################################################
#
# author  : Srikanth KS (talegari)
# license : GNU AGPLv3 (http://choosealicense.com/licenses/agpl-3.0/)
#

mjdietzx

#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda

ksopyla

# This is shorthened version of blog post 
#  http://ksopyla.com/2017/02/tensorflow-gpu-virtualenv-python3/

# update packages
sudo apt-get update
sudo apt-get upgrade

#Add the ppa repo for NVIDIA graphics driver
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update

binary1230

3/18/2021: WE HAVE MOVED: For the latest instructions on how to use the bulk wells fargo PDF downloader, 
please ignore this page and visit below:

https://github.com/binary1230/wellsfargo-bulk-PDF-statement-downloader/blob/main/README.md

awjuliani

mikeboers

import argparse
import datetime
import json
import os
import re

import requests


parser = argparse.ArgumentParser()

lukedeo

'''
hacked out deepdish.io style keras NN saving functionality

[credit] deepdish
'''
from __future__ import division, print_function, absolute_import

import numpy as np
import tables
import warnings

baraldilorenzo

##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