Sébastien Arnaud arnaudsj

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

Customize Chromebook Chrosh Shell Environment

Requirement: Chromebook, Common Sense, Commandline Ablity, 1 hour of time

Dear developers with a spare Chromebook lets inject a little personalization into your Crosh shell with custom fonts, the solarized theme, and extra secure shell options.

Also, keep in mind that the terms Chrosh, Chrosh Window, and Secure Shell all refer to various versions and extentions built around the ChromeOS terminal. Settings that affect the ChromeOS terminal are global.

Custom Fonts

Applied Functional Programming with Scala - Notes

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

Implementing the k-means jump method: Part Four

This fourth and final part of our exploration into the jump method wraps things up by following a basic application of the technique through to a search for results we can be confident in.

We'll reuse functions from rest of the series (Part One, Part Two, Part Three). You'll also want the original paper handy to refer back to their charts.

First, we're going to return to several prior data sets:

	import http.client, urllib.request, urllib.parse, urllib.error, base64
	import json
	import csv

	names = ['YOUR LIST OF NAMES']
	companies = ['YOUR LIST OF COMPANIES FOR EACH NAME']

	YOUR_API_KEY = 'YOUR_API_KEY'

	linkedin = []

	conda uninstall --force pillow -y

	# install libjpeg-turbo to $HOME/turbojpeg
	git clone https://github.com/libjpeg-turbo/libjpeg-turbo
	pushd libjpeg-turbo
	mkdir build
	cd build
	cmake .. -DCMAKE_INSTALL_PREFIX:PATH=$HOME/turbojpeg
	make
	make install

	''' Script for downloading all GLUE data.

	Note: for legal reasons, we are unable to host MRPC.
	You can either use the version hosted by the SentEval team, which is already tokenized,
	or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
	For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
	You should then rename and place specific files in a folder (see below for an example).

	mkdir MRPC
	cabextract MSRParaphraseCorpus.msi -d MRPC

	#!/usr/bin/env python3
	"""Script locating the cheapest AWS GPU compute spot instance for docker."""
	from argparse import ArgumentParser
	from datetime import datetime, timedelta
	from itertools import groupby
	from operator import itemgetter

	import boto3
	import numpy as np

	def move_to_dir(old, new):
	"""Converts using e.g. old position (1,1) and new position (2, 1) to a direction (RIGHT)"""
	if old[0] < new[0]:
	return "RIGHT"
	if old[1] < new[1]:
	return "DOWN"
	if old[0] > new[0]:
	return "LEFT"
	return "UP"

	# After Ubuntu 16.04, Systemd becomes the default.
	# It is simpler than https://gist.github.com/Doowon/38910829898a6624ce4ed554f082c4dd

	[Unit]
	Description=Jupyter Notebook

	[Service]
	Type=simple
	PIDFile=/run/jupyter.pid
	ExecStart=/home/phil/Enthought/Canopy_64bit/User/bin/jupyter-notebook --config=/home/phil/.jupyter/jupyter_notebook_config.py