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char-rnn_from_scratch

This repository describes my first try creating an char-RNN from scratch using numPy library in Python.

cool_gif_generator

This project is all about creating cool GIFs (Deal With it) using OpenCV and DL Models for Facial Landmark detection and Face detection.

cv_mnist

This repository contains 2 DL models pertaining to the famous and simple dataset called MNIST which contains handwritten digits.

dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

distributed_graph_learning_ethereum_dataset

emnlp_rebuttal

emotion_detection

Hi guys! Here I have made an Emotion Detection command line tool which classifies the input image into 7 emotion categories.

fairmot

A simple baseline for one-shot multi-object tracking

fobo

Autonomous Food Delivery Bot - Capstone Project

medical_prognostic_models

ml-model_comparison

This repository contains the work I have done on various datasets till now and various Machine Learning models are used on all of these datasets.

mot

Self-supervised learning for Multi-Object Tracking with contrastive learning, keypoint estimation, and constraint dominant set clustering

multi-variant-instructions

natural-instructions-expansion

Expanding natural instructions

neural_network_from_scratch

This Repository contains python scripts that contain neural network that constructed from scratch

nlp-on-netflixcomedy

This repository contains a fun project using Natural Language processing to understand stand-up comedy shows on Netflix

ravsehajsinghpuri.github.io

topsis_by_ravsehaj

This package is implementation of TOPSIS technique of multi-criteria decision analysis. There is a function in this package named rank_data which will accept three parameters: data_filename_path //file which contains the models and parameters list of weights separated by commas(,) list of impacts [+/-] separated by commas(,) You can install this package using following command pip install topsis_by_ravsehaj

twitter_sentimentanalysis

This repository contains 2 different approaches towards Twitter Sentiment Analysis- Logistic Regression and Naive Bayes