Bayesian Neural Network in PyTorch

A tool for downloading dropout.tv episodes

This project is a real-time traffic sign recognition system built using Python, OpenCV, and a pre-trained CNN model, capable of detecting and recognizing traffic signs from images.

Develop SNN-aware Noise Addition Layers

This repository is associated with the paper "Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling", accepted at EACL 2023.

Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).

Model to predict bank customer churn

Utilizing advanced Bidirectional LSTM RNN technology, our project focuses on accurately predicting stock market trends. By analyzing historical data, our system learns intricate patterns to provide insightful forecasts. Investors gain a robust tool for informed decision-making in dynamic market conditions. With a streamlined interface, our solution

Leveraging advanced image processing and deep learning, this project classifies plant images using a subset of the Plant Seedlings dataset. The dataset includes diverse plant species captured under varying conditions. This project holds significance within my Master's in Computer Vision at uOttawa (2023).

Imputation method for scRNA-seq based on low-rank approximation

Machine learning Algorithms for the Prediction of Successful Aging in Older Adults

This is a multiclass image classification problem. There data contains images from 6 categories 'buildings','forest','glacier','mountain','sea','street'. The aim is to develop a machine learning model that correctly classifies an input image into one of the categories

Tomato Leaf Disease Detection:Deep Learning Project

PyTorch implementation of 'Concrete Dropout'

the implementation of a multilayer perceptron

Using deep learning to predict whether students can correctly answer diagnostic questions

A Deep Learning and preprocessing framework in Rust with support for CPU and GPU.

A Library for Denoising Single-Cell Data with Random Matrix Theory