George Ng just4jc

| Title | Description

First, install all of TensorFlow and CUDA's dependencies:

$ # When prompted: Keep the local version currently installed
$ curl https://gist.github.com/kern/ab56600768b9f4fc1f72/raw/8d0b86a678d1398be2b82ac6e4ab1f8ad0698f01/install_deps.sh | sh

Wait for reboot, then run:

$ # When prompted: Scroll all the way down > accept > y > y > y > default (/usr/local/cuda-7.0) > y > n
$ curl https://gist.github.com/kern/ab56600768b9f4fc1f72/raw/8d0b86a678d1398be2b82ac6e4ab1f8ad0698f01/install_nvidia.sh | sh

$ curl https://gist.github.com/kern/ab56600768b9f4fc1f72/raw/8d0b86a678d1398be2b82ac6e4ab1f8ad0698f01/install_tensorflow.sh | sh

	pragma solidity ^0.4.24;

	// ----------------------------------------------------------------------------
	// Sample token contract
	//
	// Symbol : LCST
	// Name : LCS Token
	// Total supply : 100000
	// Decimals : 2
	// Owner Account : 0xde0B295669a9FD93d5F28D9Ec85E40f4cb697BAe

	'''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


	#------------------------------------------------------------
	# REVOLUTION ANALYTICS WEBINAR: INTRODUCTION TO R FOR DATA MINING
	# February 14, 2013
	# Joseph B. Rickert
	# Technical Marketing Manager
	#
	# BIG DATA with RevoScaleR
	#
	# Copyright: Revolution Analytics

	#!/bin/bash

	# Run this on This AMI on AWS:
	# https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-b36981d8

	# You should get yourself a fully working GPU enabled tensorflow installation.

	cd ~

	# grab cuda 7.0