Harsh188
MSCS @ UC Riverside Trying to figure out how to make Jarvis so he can solve my life problems.
@IntelRiverside, CA
Pinned Repositories
covid19-prediction
Models to predict covid-19 cases in India and USA
Deep-Co-Training
100-Days-of-ML-Pt2
100 Day ML Challenge to learn and develop machine learning products. Since this is my second time performing this challenge, this time around I will be focusing more on the production enviroment rather than the concepts and theory behind ML/DL models. I will be placing heavy emphasis on the ML pipeline and the process of taking an ML model and applying into a real-world application.
100_Days_of_ML
100 Day ML Challenge to learn and implement ML/DL concepts ranging from the basics to more advanced state of the art models.
AddressBook
This repository contains a small python address book project.
GSoC-RedHenLab-MTVSS-2022
This proposal proposes a multi-modal multi-phase pipeline to tackle television show segmentation on the Rosenthal videotape collection. The two-stage pipeline will begin with feature filtering using pre-trained classifiers and heuristic-based approaches. This stage will produce noisy title sequence segmented data containing audio, video, and possibly text. These extracted multimedia snippets will then be passed to the second pipeline stage. In the second stage, the extracted features from the multimedia snippets will be clustered using RNN-DBSCAN. Title sequence detection is possibly the most efficient path to high precision segmentation for the first and second tiers of the Rosenthal collection (which have fairly structured recordings). This detection algorithm may not bode well for the more unstructured V8+ and V4 VCR tapes in the Rosenthal collection. Therefore the goal is to produce accurate video cuts and split metadata results for the first and second tiers of the Rosenthal collection.
Introduction-to-Machine-Learning
This repo will house all our course material and code snippets from the Introduction to Machine Learning Class
RecycleNet
Slot11_Intro-to-ML
Text_Summarizer
This project is a part of my semester long 'mini-project' course at PES University. With the guidance of Dr. S Natarajan and the help of my fellow colleague Zayd J we were able to deploy google's state of the art abstractive text summarization model, PEGASUS, onto the internet for anyone to utilize.
Harsh188's Repositories
Harsh188/100-Days-of-ML-Pt2
100 Day ML Challenge to learn and develop machine learning products. Since this is my second time performing this challenge, this time around I will be focusing more on the production enviroment rather than the concepts and theory behind ML/DL models. I will be placing heavy emphasis on the ML pipeline and the process of taking an ML model and applying into a real-world application.
Harsh188/100_Days_of_ML
100 Day ML Challenge to learn and implement ML/DL concepts ranging from the basics to more advanced state of the art models.
Harsh188/GSoC-RedHenLab-MTVSS-2022
This proposal proposes a multi-modal multi-phase pipeline to tackle television show segmentation on the Rosenthal videotape collection. The two-stage pipeline will begin with feature filtering using pre-trained classifiers and heuristic-based approaches. This stage will produce noisy title sequence segmented data containing audio, video, and possibly text. These extracted multimedia snippets will then be passed to the second pipeline stage. In the second stage, the extracted features from the multimedia snippets will be clustered using RNN-DBSCAN. Title sequence detection is possibly the most efficient path to high precision segmentation for the first and second tiers of the Rosenthal collection (which have fairly structured recordings). This detection algorithm may not bode well for the more unstructured V8+ and V4 VCR tapes in the Rosenthal collection. Therefore the goal is to produce accurate video cuts and split metadata results for the first and second tiers of the Rosenthal collection.
Harsh188/Slot11_Intro-to-ML
Harsh188/GSoC-TensorFlow-2020
Google Summer of Code 2020 with TensorFlow: Final Work Product
Harsh188/inaSpeechSegmenter
CNN-based audio segmentation toolkit. Allows to detect speech, music and speaker gender. Has been designed for large scale gender equality studies based on speech time per gender.
Harsh188/PersonalWebsite
Harsh188/SentimentAnalysis
This is a small NLP project which uses sentiment analysis and machine learning to classify words with positive or negative connotations.
Harsh188/tensorflow
An Open Source Machine Learning Framework for Everyone
Harsh188/Text_Summarizer
This project is a part of my semester long 'mini-project' course at PES University. With the guidance of Dr. S Natarajan and the help of my fellow colleague Zayd J we were able to deploy google's state of the art abstractive text summarization model, PEGASUS, onto the internet for anyone to utilize.
Harsh188/addons
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
Harsh188/RecycleNet
Harsh188/Tic-Tac-Toe
Web technologies final semester project.
Harsh188/AttendenceChecker-CS205
Harsh188/Best-README-Template
An awesome README template to jumpstart your projects!
Harsh188/Colosseum
Open source simulator for autonomous robotics built on Unreal Engine with support for Unity
Harsh188/decord
An efficient video loader for deep learning with smart shuffling that's super easy to digest
Harsh188/docs
TensorFlow documentation
Harsh188/FTNet
Pytorch implementation of FTNet for Semantic Segmentation on SODA, SCUT Seg, and MFN Datasets
Harsh188/GraphCoReg
Graphs are widely in use to model related instances of data attributed with properties providing rich spatial information. While a lot of classical graph-related problems have been solved with the advent of Graph Neural Networks (GNN), Spatio-Temporal data poses a new challenge. We propose GraphCoReg: a novel methodology to perform regression on spatio-temporal data, in a Semi-Supervised Learning (SSL) setting using co-training. Our co-training approach exploits two views of the dataset using two temporal Graph Neural Networks (GNNs) - an Attention-based GNN (A3TGCN) and a Long Short Term Memory GNN (GCLSTM). Additionally, methodologies to incrementally add the pseudo-targets to training data have been described. We finally compare the performance of the semi-supervised model with equivalent supervised models. This approach has been tested on the MetrLA dataset for traffic forecasting.
Harsh188/harsh188.github.io
Harsh188/keras
Deep Learning for humans
Harsh188/Paper-a-Week
Harsh188/portfolio
Harsh188/RapidAnnotator-2.0
With Red Hen Lab’s Rapid Annotator we try to enable researchers worldwide to annotate large chunks of data in a very short period of time with least effort possible and try to get started with minimal training.
Harsh188/sklearn-ann
Integration with (approximate) nearest neighbors libraries for scikit-learn + clustering based on with kNN-graphs.
Harsh188/SSP-KVS
Harsh188/tfx-addons
Developers helping developers. TFX-Addons is a collection of community projects to build new components, examples, libraries, and tools for TFX. The projects are organized under the auspices of the special interest group, SIG TFX-Addons. Join the group at http://goo.gle/tfx-addons-group
Harsh188/TheBakedBrownies
Harsh188/thesuhas