Yiqun Hu codingneo

install.packages("devtools")

library(devtools)

dev_mode(on=T)

install_github("hadley/ggplot2")

# use dev ggplot2 now

Fast Parallel Stochastic Gradient (FPSG) For Matrix Factorization

Reference

Installation

You can install SparkR from github directly.

if (!require('devtools')) install.packages('devtools')
devtools::install_github('apache/[email protected]', subdir='R/pkg')

You should choose tag (v1.4.0 above) corresponding to the version of Spark you use. You can find a full list of tags on the project page or directly from R using GitHub API:

jsonlite::fromJSON("https://api.github.com/repos/apache/spark/tags")$name

#Deep Learning Model

Review

Zachary C. Lipton, John Berkowitz and Charles Elkan, A Critical Review of Recurrent Neural Networks for Sequence Learning

Deep Architecture

Rupesh Kumar Srivastava, Klaus Greff and Jurgen Schmidhuber, Highway Networks, 2015.
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun, Deep Residual Learning for Image Recognition, 2015
Wojciech Zaremba, Ilya Sutskever and Oriol Vinyals, Recurrent Neural Network Regularization, 2014

##Reference

M A H Dempster and E A Medova, Planning for Retirement: Asset Liability Management for Individuals, Asset Liability Management 2010 Yearbook
E. A. MEDOVA, J. K. MURPHY, A. P. OWEN and K. REHMAN, Individual Asset Liability Management Quantitative Finance, 2008

Problem

Reference

Learning From Aggregates

Novi Quadrianto, Alex J. Smola, Tiberio S. Caetano and Quoc V. Le, Estimating Labels from Label Proportions, JMLR 2009.
Giorgio Patrini, Richard Nock, Paul Rivera and Tiberio Caetano, (Almost) No Label No Cry, NIPS 2014 [Supplement] [Code].
Richard Nock, Giorgio Patrini and Arik Friedman, Rademacher Observations, Private Data and Boosting, ICML 2015, [PDF] [Ariv Long Version].

Differential Privacy

Launch the g2.2xlarge instance, select a security group with SSH rule
ssh to the instance

ssh -i [pem file] ubuntu@[Public DNS of the instance]

Update the locale

export LC_ALL="en_US.UTF-8"
export LC_CTYPE="en_US.UTF-8"
sudo dpkg-reconfigure locales

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):

	"""
	The MIT License (MIT)

	Copyright (c) 2015 Alec Radford

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is