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# coding=UTF-8
from __future__ import division
import nltk
import re
import requests
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An implementation of the `Local reparameterization trick`
from Kingma & Wellings and
Bayesian RNN
from Fortunato, Blundell & Vinyals
import os
import time
import copy
from os.path import join as pjoin
View README.rst

Elasticsearch CLI

Experimental CLI interface for the helpers in the `python library`_.

Main purpose is to expose the bulk functionality to enable rapid loading of data into an elasticsearch cluster. Combined with the scan command it can also be used to reindex data from elasticsearch into a different index or cluster.

View schools.stan
data {
int<lower=0> J; // number of schools
real y[J]; // estimated treatment effects
real<lower=0> sigma[J]; // s.e. of effect estimates
parameters {
real mu;
real<lower=0> tau;
real eta[J];
# This work is licensed under the Creative Commons Attribution 3.0 United
# States License. To view a copy of this license, visit
# or send a letter to Creative
# Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.
# from
# Orignial Author Oran Looney <>
/usr/bin/ruby -e "$(curl -fsSL"
brew update
brew install cmake
View SparkSQLJira.scala
package com.databricks.spark.jira
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.sources.{TableScan, BaseRelation, RelationProvider}
View gist:12f58e684768529b72d7d89f0440ea5e
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hadoop.conf.Configuration
case class S3File(path: String, isDir: Boolean, size: Long) {
def children = listFiles(path)
def listFiles(path: String): Seq[S3File] = {
val fs = FileSystem.get(new, new Configuration())
fs.listStatus(new Path(path)).map(s => S3File(s.getPath.toString, s.isDir, s.getLen))
class AC_Network():
def __init__(self,s_size,a_size,scope,trainer):
with tf.variable_scope(scope):
#Input and visual encoding layers
self.inputs = tf.placeholder(shape=[None,s_size],dtype=tf.float32)
self.imageIn = tf.reshape(self.inputs,shape=[-1,84,84,1])
self.conv1 = slim.conv2d(activation_fn=tf.nn.elu,
self.conv2 = slim.conv2d(activation_fn=tf.nn.elu,
View Deep Layer Visualization.ipynb
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