Nirnay Roy's Projects
Project on Fermi Acceleration in 2d billiards
Audio API for streaming raw data to speakers
Automatic Data Science
Implementation of denoise, sparse, contractive, variational autoencoder (VAE) and Beta-VAE in Tensorflow 2.0.
:memo: An awesome Data Science repository to learn and apply for real world problems.
A collection of drug discovery, classification and representation learning papers with deep learning.
Inference Model for BertSum
Tensorflow 1.14 for Bert, Bert UniLM, ALBert
Interactive Web Plotting with Bokeh in IPython notebook
Project for DSE-301
PyTorch Implementation of DCGAN trained on the CelebA dataset.
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
This repo contains deeplearning.ai Course 05 Week 1 notebooks. (Building RNNs and language modeling)
Python implementation of Stacked Denoising Autoencoders for unsupervised learning of high level feature representation
python, scala, and pyspark code for few dimensional reduction algorithms
Data augmentation for NLP, presented at EMNLP 2019
Email Summarization Flask App
ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution (Third Region)
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
A modified reimplemented in pytorch of inpainting model in Free-Form Image Inpainting with Gated Convolution [http://jiahuiyu.com/deepfill2/]
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras
Open source UI framework written in Python, running on Windows, Linux, macOS, Android and iOS
Inference code for LLaMA models
Introduction to manifold learning - mathematical theory and applied python examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)