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sudo apt-get remove scala-library scala
sudo wget www.scala-lang.org/files/archive/scala-2.10.4.deb
sudo dpkg -i scala-2.10.4.deb
sudo apt-get update
sudo apt-get install scala
wget http://scalasbt.artifactoryonline.com/scalasbt/sbt-native-packages/org/scala-sbt/sbt/0.12.4/sbt.deb
sudo dpkg -i sbt.deb
sudo apt-get update
sudo apt-get install sbt

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WITH Customers AS (
  (SELECT 1 AS Customer, 1 AS Tea, 1 AS Scones) UNION ALL 
  (SELECT 2 AS Customer, 0 AS Tea, 1 AS Scones) UNION ALL
  (SELECT 3 AS Customer, 0 AS Tea, 0 AS Scones) UNION ALL
  (SELECT 4 AS Customer, 0 AS Tea, 0 AS Scones) UNION ALL
  (SELECT 5 AS Customer, 1 AS Tea, 1 AS Scones) UNION ALL
  (SELECT 6 AS Customer, 1 AS Tea, 0 AS Scones) UNION ALL  
  (SELECT 7 AS Customer, 1 AS Tea, 0 AS Scones) UNION ALL 
  (SELECT 8 AS Customer, 1 AS Tea, 1 AS Scones) UNION ALL
  (SELECT 9 AS Customer, 1 AS Tea, 0 AS Scones) UNION ALL

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// Licensed under the Apache License. See footer for details.
var express = require('express');
var passport = require('passport');
var app = express();

var bodyParser = require('body-parser');
app.use(bodyParser());

var cookieParser = require('cookie-parser');
app.use(cookieParser());

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# Setup the model
x <- tf$placeholder(tf$float32, shape(NULL, 1024L))
W <- tf$Variable(tf$zeros(shape(1024L, 10L)))
b <- tf$Variable(tf$zeros(shape(10L)))
t <- tf$nn$softmax(tf$matmul(x, W) + b)
# Specify the loss and optimizer
t_ <- tf$placeholder(tf$float32, shape(NULL, 10L))
xent <- tf$reduce_mean(-tf$reduce_sum(t_ * log(t), reduction_indices=1L))
batch_step <- tf$train$GradientDescentOptimizer(0.5)$minimize(xent)
titanic_train_ds <- csv_record_spec("titanic.train.csv")

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Using TensorFlow in R

Chapter 1 - Your first "Hello World" neural network with TensorFlow in R

• Lesson 1.1 - Getting started with TensorFlow

  • A learning objective: Create tensors in R and use the building blocks of TensorFlow APIs

• Lesson 1.2 - Implementing graphs and loops in TensorFlow

  • A learning objective: Implement a multilayer neural network and evaluate it for predictions against a sample dataset

• Lesson 1.3 - Training a neural network with gradient descent

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package org.deeplearning4j.examples.feedforward.xor;

import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;

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package org.deeplearning4j.examples.feedforward.xor;

import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.distribution.UniformDistribution;

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<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>org.deeplearning4j</groupId>
    <artifactId>dl4j-spark-cdh5-examples</artifactId>
    <version>1.0-SNAPSHOT</version>
    <packaging>jar</packaging>

    <name>dl4j-spark-cdh5-examples</name>

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 CREATE TABLE "address" ("address" "text", "city" "text", "state" "text", "postalCode" "text", "country" "text", "lat" "float", "lon" "float");