karimkhanp’s gists

karimkhanp / nlp_defs

Last active August 29, 2015 14:05

Important terminologies for mlp stuff

	Freebase - is a large collaborative knowledge base consisting of metadata composed mainly by its community members. It is an online collection of structured data harvested from many sources, including individual 'wiki' contributions
	-> The MQL Read and MQL Write APIs provides access to the Freebase database using the Metaweb query language (MQL).

	DBpedia - (from "DB" for "database") is a project aiming to extract structured content from the information created as part of the Wikipedia project. This structured information is then made available on the World Wide Web.[1] DBpedia allows users to query relationships and properties associated with Wikipedia resources, including links to other related datasets
	-> Data is accessed using an SQL-like query language for RDF called SPARQL. For example, imagine you were interested in the Japanese shōjo manga series Tokyo Mew Mew, and wanted to find the genres of other works written by its illustrator. DBpedia combines information from Wikipedia's entries on

karimkhanp / datasets

Created August 28, 2014 05:14

This gist provides you various free data source for data processing

	https://www.cia.gov/library/publications/download/
	http://flowingdata.com/2009/10/01/30-resources-to-find-the-data-you-need/

karimkhanp / bigdata_resource

Last active April 6, 2022 13:48

Bigdata resources - Do I miss something. Add and make it richer

	Bigdata is like combination of bunch of subjects. Mainly require programming, analysis, nlp, MLP, mathematics.

	To see links, Go : http://www.quora.com/What-are-some-good-sources-to-learn-big-data
	Here are bunch of courses I came accross:

	Introduction to CS Course
	Notes: Introduction to Computer Science Course that provides instructions on coding.
	Online Resources:
	Udacity - intro to CS course,
	Coursera - Computer Science 101

karimkhanp / dictionary

Last active August 29, 2015 14:09

dictiona list

	http://scrapmaker.com/home
	http://www.momswhothink.com/reading/list-of-verbs.html

karimkhanp / client

Created February 5, 2015 06:04

gearman client multiple worker

	import json
	import gearman
	import time
	import sys
	def check_request_status(job_request):
	if job_request.complete:
	#print len(job_request.result)
	#data = json.loads(job_request.result)
	print "Job %s finished! Result: %s - %s" % (job_request.job.unique, job_request.state, job_request.result)
	elif job_request.timed_out:

karimkhanp / worker

Created February 5, 2015 06:05

gearman worker 1 multiple worker

	import gearman

	gm_worker = gearman.GearmanWorker(['localhost:4730'])

	def task_listener_reverse(gearman_worker, gearman_job):
	print 'Reversing string: ' + gearman_job.data

	return gearman_job.data[::-1]

	# gm_worker.set_client_id is optional

karimkhanp / sentiment analysis_full

Last active May 23, 2018 20:52

sentiment analysis resources combined



	Supervised - Machine learning based
	Unsupervised - Lexicon based

	English lang:
	http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon
	http://www.cs.uic.edu/~liub/FBS/opinion-lexicon-English.rar - lexicon sentiment dictionary
	http://www.cs.uic.edu/~liub/FBS/opinion-lexicon-English.rar
	https://sites.google.com/site/datascienceslab/projects/multilingualsentiment

karimkhanp / learning

Last active October 13, 2017 09:33

Learning

	MLP :
	svm - https://en.wikipedia.org/wiki/Support_vector_machine
	neural network - https://www.quora.com/What-is-a-simple-explanation-of-how-artificial-neural-networks-work-1/answer/Annalyn-Ng?srid=XpXu
	- https://www.quora.com/What-is-a-simple-explanation-of-how-artificial-neural-networks-work-1/answer/Chris-Nicholson-1?srid=XpXu
	perceptron model
	k-mean
	naive bayes -
	https://web.stanford.edu/class/cs124/lec/naivebayes.pdf
	http://stackoverflow.com/questions/10059594/a-simple-explanation-of-naive-bayes-classification
	http://deeplearning4j.org/sentiment_analysis_word2vec.html

karimkhanp / ds1

Created May 5, 2015 18:56

full

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karimkhanp / temp1.py

Created October 19, 2015 09:19

	from __future__ import division
	from math import log, exp
	from operator import mul
	from collections import Counter
	import os
	import pylab
	import cPickle


	class MyDict(dict):

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