John Robenalt robenalt

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Efficient Estimation of Word Representations in Vector Space

Introduction

Introduces techniques to learn word vectors from large text datasets.
Can be used to find similar words (semantically, syntactically, etc).
Link to the paper
Link to open source implementation

Model Architecture

Create PDF files from Markdown sources in OSX

When [Markdown][markdown] appeared more than 10 years ago, it aimed to make it easier to express ideas in an easy-to-write plain text format. It offers a simple syntax that takes the writer focus away from the formatting, thus giving her time to focus on the actual content.

The market abunds of editors to be used for help with markdown. After a few attempts, I settled to Sublime and its browser preview plugin, which work great for me and have a small memory footprint to accomplish that. To pass the results around to other people, less technical, a markdown file and a bunch of images is not the best approach, so converting it to a more robust format like PDF seems like a much better choice.

[Pandoc][pandoc] is the swiss-army knife of converting documents between various formats. While being able to deal with heavy-weight formats like docx and epub, we will need it for the more lightweight markdown. To be able to generate PDF files, we need LaTeX. On OSX, the s

	on write_to_file(this_data, target_file, append_data)
	try
	set the target_file to the target_file as string
	set the open_target_file to open for access file target_file with write permission
	if append_data is false then set eof of the open_target_file to 0
	write this_data to the open_target_file starting at eof
	close access the open_target_file
	return true
	on error
	try

	# Defaults / Configuration options for homebridge
	# The following settings tells homebridge where to find the config.json file and where to persist the data (i.e. pairing and others)
	HOMEBRIDGE_OPTS=-U /var/lib/homebridge

	# If you uncomment the following line, homebridge will log more
	# You can display this via systemd's journalctl: journalctl -f -u homebridge
	# DEBUG=*

	""" Deep Auto-Encoder implementation

	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b) = x'

	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k

	import org.apache.spark.mllib.linalg.distributed.RowMatrix
	import org.apache.spark.mllib.linalg._
	import org.apache.spark.{SparkConf, SparkContext}

	// To use the latest sparse SVD implementation, please build your spark-assembly after this
	// change: https://github.com/apache/spark/pull/1378

	// Input tsv with 3 fields: rowIndex(Long), columnIndex(Long), weight(Double), indices start with 0
	// Assume the number of rows is larger than the number of columns, and the number of columns is
	// smaller than Int.MaxValue

	tell application "Microsoft Outlook"
	query freebusy exchange account 1 for attendees {"[email protected]"} range start time date "Thursday, September 8, 2016 at 12:00:00 AM" range end time date "Friday, September 9, 2016 at 12:00:00 AM" interval 60
	end tell