Dinesh M mdinesh9

Python Number Conversion Chart

From	To	Expression

Frontend Development

Attention: the list was moved to

https://github.com/dypsilon/frontend-dev-bookmarks

This page is not maintained anymore, please update your bookmarks.

Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

Introduce Gizzard of Twitter - 트위터의 새로운 분산 관리 라이브러리 Gizzard를 소개합니다.
The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 Seconds
Scaling Twitter: Making Twitter 10000 Percent Faster

On a recent project, I ran into an issue with Python Selenium webdriver. There's no easy way to open a new tab, grab whatever you need and return to original window opener.

Here's a couple people who ran into the same complication:

So, after many minutes (read about an hour) of searching, I decided to do find a quick solution to this problem.

General Background and Overview

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

Feature Learning

Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
Scaling Learning Algorithms towards AI Benjio & LeCun
A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

Deep Learning

Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
[Understanding

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	import matplotlib.pyplot as plt
	import numpy as np
	column_labels = list('ABCD')
	row_labels = list('WXYZ')
	data = np.random.rand(4,4)
	fig, ax = plt.subplots()
	heatmap = ax.pcolor(data, cmap=plt.cm.Blues)

	# put the major ticks at the middle of each cell
	ax.set_xticks(np.arange(data.shape[0])+0.5, minor=False)

	all: prereq build
	prereq:
	apt-get -y install git
	apt-get -y install curl
	apt-get -y install openjdk-7-jdk
	apt-get -y install maven
	git clone https://github.com/apache/spark.git
	cd spark; git checkout tags/v1.0.1
	build:
	export SPARK_HADOOP_VERSION=2.4.0

	// XPath CheatSheet
	// To test XPath in your Chrome Debugger: $x('/html/body')
	// http://www.jittuu.com/2012/2/14/Testing-XPath-In-Chrome/


	// 0. XPath Examples.
	// More: http://xpath.alephzarro.com/content/cheatsheet.html


	'//hr[@class="edge" and position()=1]' // every first hr of 'edge' class