sandeshregmi Goto Github PK

a-collection-of-important-tasks-in-pytorch

Everyday things people use in Pytorch. No need to spend hours reading Pytorch forums trying to find them!

accidentaldrugdeaths-datastorytelling

this repo provides some earlier visualizations I had done in R.

anapplicationforclassifyingdepression

Abstract Depression brings significant challenges to the overall global public health. Each day, millions of people suffered from depression and only a small fraction of them undergo proper treatments. In the past, doctors will diagnose a patient via a face to face session using the diagnostic criteria that determine depression such as the Depression DSM-5 Diagnostic Criteria. However, past research revealed that most patients would not seek help from doctors at the early stage of depression which results in a declination in their mental health condition. On the other hand, many people are using social media platforms to share their feelings on a daily basis. Since then, there have been many studies on using social media to predict mental and physical diseases such as studies about cardiac arrest (Bosley et al., 2013), Zika virus (Miller, Banerjee, Muppalla, Romine, & Sheth, 2017), prescription drug abuse (Coppersmith, Dredze, Harman, Hollingshead, & Mitchell, 2015) mental health (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016) and studies particularly about depressive behavior within an individual (Kiang, Anthony, Adrian, Sophie, & Siyue, 2015). This research particularly focuses on leveraging social media data for detecting depressive thoughts among social media users. In essence, this research incorporated text analysis that focuses on drawing insights from written communication in order to conclude whether a tweet is related to depressive thoughts. This research produced a web application that performs a real-time enhanced classification of tweets based on a domain-specific lexicon-based method, which utilizes an improved dictionary that consists of depressive and non-depressive words with their associated orientations to classify depressive tweets. Problem understanding or Business Understanding Depression is the main cause of disability worldwide (De Choudhury et al., 2013). Statistically, an estimation of nearly 300 million people around the world suffers from depression. Shen et al (2017) mentioned that approximately 70% of people with early stages of depression would not consult a clinical psychologist. Many people are utilizing social media sites like Facebook and Instagram to disclose their feelings. This research persists the hypothesis that there are similarities between the mental state of an individual and the sentiment of their tweets and investigated the potentiality of social media (like twitter) as a data source for classifying depression among individuals.

annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

anomalydetection-with-autoencoder

applied-machine-learning-in-python

applied_machine_learning

articles

References to the Medium articles

auto_ml

[UNMAINTAINED] Automated machine learning for analytics & production

autoanalytics

Automating the process of data pre-processing and model training in Machine Learning.

autoencoder

Autoencoder on tabular data

avenir

Set of Machine Learning and Stochastic Optimazion tools based on Hadoop, Spark and Storm https://pkghosh.wordpress.com/

awesome-cv

:page_facing_up: Awesome CV is LaTeX template for your outstanding job application

awesome-mlops

A curated list of references for MLOps

b2b_bid_pricing_cm

end to end predictive modeling framework leveraging multiple ML techniques for bid pricing

behaviouralanalysis

Sentiment analysis (sometimes known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

bert-pytorch

Google AI 2018 BERT pytorch implementation

book_on_python_algorithms_and_data_structure

🪐 Book on Python, Algorithms, and Data Structures. 🪐

caret

caret (Classification And Regression Training) R package that contains misc functions for training and plotting classification and regression models

causal_feature_selection

Causal Feature Selection Tutorial for AMIA2018

clinical-outcome-prediction

Code for the EACL 2021 Paper: Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration

clinicalbert-deep-learning--predicting-hospital-readmission-using-transformer

Blog post on Medium

cms-fraud-detection

Fraud detection algorithm using Autoencoders and Stacked Autoencoders to detect fraudulent physicians in CMS Part B claims data

cmshcc

This R package calculates CMS-HCC risk scores as an alternative to using SAS

code_snippets

commondatamodel

Definition and DDLs for the OMOP Common Data Model (CDM)

community-lfm

Code for predicting risky behavior in social communities.

coronavirus_visualization_and_prediction

This repository tracks the spread of the novel coronavirus, also known as SARS-CoV-2. It is a contagious respiratory virus that first started in Wuhan in December 2019. On 2/11/2020, the disease is officially named COVID-19 by the World Health Organization.

courses

This is repo of all Data Responsibly related courses.

covid-19-data-science

Welcome to Glacier Data Project. A post-wuhan2020 project for data science