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*.md
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libraries

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This is my recommended path for learning Haskell.

Something to keep in mind: don't sweat the stuff you don't understand immediately. Just keep moving.

Primary course

http://www.seas.upenn.edu/~cis194/lectures.html Brent Yorgey's course is the best I've found so far and replaces both Yann Esposito's HF&H and the NICTA course. This course is particularly valuable as it will not only equip you to write Haskell but also help you understand parser combinators.

Exercises for practice

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How to install cGit on Nginx (Ubuntu server)

Step-by-step installtion of cGit on nginx without funky rewrite rules.

Pre-requisites

This is for :

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#!/usr/bin/env python

"""
ODT2PANDOC
==========

ODT2PANDOC is a slight variant of ODT2TXT. Right now, the only differences are that it generates ATX style headers and uses *asterisks* for italics, neither of which is in any way specific to pandoc's extended markdown.

ODT2TXT
=======

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Business Models

Advertising

Models	Examples
Display ads	Yahoo!
Search ads	Google

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#!/usr/bin/env python
# A github post-receive hook handler, runs some shell command on each HTTP POST to PORT.
# github-listener.py PORT 'SOME SHELL COMMAND'
 
import sys
from subprocess import *
from flask import Flask
 
def system(cmd):
    return ''.join(Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE, close_fds=True).communicate())

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#define _XOPEN_SOURCE 700
#include <signal.h>
#include <unistd.h>

int main()
{
	sigset_t set;
	int status;

	if (getpid() != 1) return 1;

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1. 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

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1. 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

2. 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 the difficulty of training deep feedforward neural networks](http://jmlr.org/proceedings/papers/v9/glorot10a/gloro

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