urmilkadakia Goto Github PK
Name: Urmil kadakia
Type: User
Bio: Pursuing masters in Computer Science at Stony Brook University.
Location: New York, USA
Name: Urmil kadakia
Type: User
Bio: Pursuing masters in Computer Science at Stony Brook University.
Location: New York, USA
kaggle competition to predict how much GStore customers will spend.
Implementation of Tree Structured LSTM and Attention Mechanism Models for the task of Sentiment Analysis on Stanford Sentiment Treebank
kaggle competition to predict a rider's taxi fare.
A Streaming Algorithm for Estimating k-mer Counts with Optimal Space Usage in Parallel
Prediction of rainfall which varies both spatially and temporally is extremely challenging. Infrared and visible spectral data from satellites have been extensively used for rainfall prediction. In this study, two deep learning methods MLP and LSTM are discussed at length for predicting precipitation at a fine spatial (10km × 10km) and temporal (hourly) resolution for the state of Gujarat. These methods are applied by using the multispectral (VIS, SWIR, MIR, WV, TIR1, TIR2) channel data such as cloud top temperature and radiance values of the INSAT-3D satellite (ISRO) as features for the model. Textural features of satellite images are incorporated by considering mean and standard deviation of each pixel’s neighbourhood. Rainfall also heavily depends on the elevation and vegetation of earth’s surface so we have used SRTM DEM and AWIFS NDVI respectively. Measurements of actual rainfall are obtained from AWS (point source stations) and TRMM (10km × 10km resolution). First dataset contains only TIR1 band temperature and AWS rainfall data for training but the second dataset includes multispectral channel data and TRMM rainfall data which brought about great improvement in results. For each data- set, a comparison between MLP and LSTM models is discussed here. We were able to classify the rainfall into nil (0mm), low ( < 2mm), medium ( > = 2mm and < 5mm) and high ( > = 5 mm) with a high accuracy. Metrics like accuracy, precision, recall and fscore have been computed to get better insights about the dataset and its corresponding outcome. Our results show that LSTM performs significantly better than MLP for any given balanced class data-sets.
The purpose of this project is to construct a ranking metric to evaluate academic papers and researchers using available data in citation network dataset.
A python project to study patterns in identity change as revealed by the edits users make to their online profiles using data from Reddit.
A python project to study patterns in identity change as revealed by the edits users make to their online profiles using data from Twitter.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.