romilbansal Goto Github PK

cassovary

Cassovary is a simple big graph processing library for the JVM

entity-recognition-and-disambiguation-challenge

entitylinking

erd-challenge

Entity Recognition and Disambiguation Challenge

expandurl

A java project that can expand the short URLs from various sources and return the original long URL.

flickrdesktop

Flickr User Interface

hashtag-segmentation

Experimenting with different techniques, new and old to reach at a more semantically appealing hashtag segmentation.

iir

Machine Learning / Natural Language Processing / Information Retrieval

libsvm-androidjni

A ported version of libsvm for Android jni environment

likelines-player

LikeLines: Timecode-level Feedback for Web Videos through User Interactions

microposts2014

Microposts Challenge 2014

mrec

mrec recommender systems library

networklog

Android Network Log Monitor

nirbhaya

pdf.js

PDF Reader in JavaScript

ranklib

A Learning to Rank Library

reinforcementlearning

Code for reinforcement learning experiments I have been doing. PS: Some of the codes have be borrowed from other repos on github and have been modified for my use case

reslve

reslve-1

A collection of snippets for modeling user interest in Wikipedia

stanford-project-predicting-stock-prices-using-a-lstm-network

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

stock-prediction-using-lstm

This Project is predicting stocks for 32 companies with error less than 1% using LSTM Networks.

stockpriceprediction

Realtime stock price prediction using Reinforcement Learning

summarize.py

A python script for summarizing articles using nltk

tindersimpleswipecards

the basics of a Tinder-like swipeable cards interface based off of http://guti.in/articles/creating-tinder-like-animations/

trec-kba

This project contains some Hadoop code for working with the TREC Knowledge Base Acceleration dataset. In particular, it provides classes to read/write topic files, read/write run files, and expose the documents in the Thrift files as Hadoop-readable objects.

twitter_nlp

UW Twitter NLP Tools

twitterdemo

Twitter-Demo Project

vivagraphjs

Graph drawing library for JavaScript