SharuGitHubSpace
https://www.linkedin.com/in/saranya-krishnasami-balaji-1158948b/
Graduate Student at University of OttawaOttawa, Canada
Pinned Repositories
CSI-5155-Machine-Learning-Assignment-2
CSI 5155 - Machine Learning - Evaluation of Learning Task
CSI-5155-Machine-Learning-Assignment-4
CSI-5155-Machine-Learning-Assignment-2
CSI5195-EthicsinAI-FinalReport
CSI 5195 - Ethics in Artificial intelligence - Final report - Supporting Document 1
E2E-KGQA-DataPipeline
LSTM-PICO-Detection
Code for LSTM based model for PICO element detection
MachineLearningAlgorithmsExercise
Practice ML Algorithms
ML_Assignment1_DrugConsumptionAnalysis
Drug Consumption – Decade based Classification problem
NLP-FinalProject-PICO-LSTMModel
NLP-PICO-Labelling---Bio-Clinical-BERT
SentenceEmbedding_InferSent
Sentence Embedding using InferSent
SharuGitHubSpace's Repositories
SharuGitHubSpace/CSI-5155-Machine-Learning-Assignment-2
CSI 5155 - Machine Learning - Evaluation of Learning Task
SharuGitHubSpace/CSI-5155-Machine-Learning-Assignment-4
CSI-5155-Machine-Learning-Assignment-2
SharuGitHubSpace/CSI5195-EthicsinAI-FinalReport
CSI 5195 - Ethics in Artificial intelligence - Final report - Supporting Document 1
SharuGitHubSpace/E2E-KGQA-DataPipeline
SharuGitHubSpace/LSTM-PICO-Detection
Code for LSTM based model for PICO element detection
SharuGitHubSpace/MachineLearningAlgorithmsExercise
Practice ML Algorithms
SharuGitHubSpace/ML_Assignment1_DrugConsumptionAnalysis
Drug Consumption – Decade based Classification problem
SharuGitHubSpace/NLP-FinalProject-PICO-LSTMModel
SharuGitHubSpace/NLP-PICO-Labelling---Bio-Clinical-BERT
SharuGitHubSpace/SentenceEmbedding_InferSent
Sentence Embedding using InferSent
SharuGitHubSpace/SentimentAnalysis
Sentiment Analysis with three models . General model that can detect Potive ,Neutral and Negative Labels. While the Review model can detect a the polarity of sentance by Very negative, Negative , Neutral , ostive and very postive lables. The fine grained EMotion mdoel can detect upto 29 emotions such as Joy , Admiration, Anger , love, surprise.
SharuGitHubSpace/TimeSeriesForecastingPractice