Beat the baseline retrieval performance(here) for the Recipe1M cross-modal food recipe retrieval task by 80% by improving on the feature extraction pipeline

Improved retrieval performance by learning shared multi-modal representations using CCA and non-linear neural networks trained using Triplet Loss

Enhanced the explainability of the system by incorporating Vision Transformers and cross-modal attention when learning shared representations

Obtained a 3% improvement on existing results by implementing the paper here from scratch and performing hyperparameter tuning on all the three CF approaches: KNN, SVD and SVDpp.

Experimented with neural CF approaches employing Neural Matrix Factorization as an extension of the paper and obtained comparable results of RMSE=1.232 and MAE=0.9569

Reproduced and improved the results of open set and closed set logo detection from here by a factor of 12% using YOLOv5 detector

Obtained a classification accuracy of 22.56% for 47 classes of the Flickr-47 dataset using a logo classification architecture consisting of YOLOv5 and template matching focused on both abstract and textual logos

Built a 11-layer deep autoencoder neural network using residual connections that colorizes black and white images

Trained the network on 10,000 images from FloydHub and deployed online via Streamlit

Analyzed a 55-million-rows dataset on the cloud to determine varying trends in taxi fares across both location and time

Augmented the data with features that help analyze trips to and from airports and across different boroughs of NY City

Predicted taxi fares to an RMSE score of 4.28 by training a Random Forest model on the augmented dataset

Developed and deployed a customizable application that uses pandas and Exploratory Data Analysis to suggest an optimal team to be entered into the Fantasy Premier League fantasy soccer game

Ranked in the top 2% in worldwide ranking among 8.2 million players by leveraging this application

Built a text simplification system that can work on text and simplify it by removing difficult-to-understand words

Modeled and trained Transformer models that internalized the semantics of and recognized complex words in input

Improved the performance of the application by preceding the transformer architecture with a Complex Word Identification (90.23% accuracy) model that flagged the complex words beforehand

Determined the ages of abalones (snails) using classification techniques and leveraging their physical characteristics

Improved the accuracy of determining age using regression techniques and obtained a MAE of 0.936

Concluded that the dataset is not large enough to get the desired MAE of 0.5 implying correct age prediction

Expanded the ‘Space Invader’ game to include three modes of play: Arcade, Timed and Survival

Developed an Android application that acts as a combination of a reminder app and a notes app

Published the app on Google Play Store, and currently has 50+ installs with a rating of 4.6