Name: Saransh Gupta
Type: User
Company: American Express
Bio: Experienced in Computational Biology, Machine learning, Data-Analytics, Deep Learning, Computer Vision, Virtual Reality
Twitter: Saransh89597284
Location: Gurugram
Blog: https://saranshqm.github.io/
Saransh Gupta's Projects
A2OJ Ladder checkbox
The medical literature is enormous. Pubmed, a database of medical publications maintained by the U.S. National Library of Medicine, has indexed over 23 million medical publications. Further, the rate of medical publication has increased over time, and now there are nearly 1 million new publications in the field each year, or more than one per minute. The large size and fast-changing nature of the medical literature has increased the need for reviews, which search databases like Pubmed for papers on a particular topic and then report results from the papers found. While such reviews are often performed manually, with multiple people reviewing each search result, this is tedious and time consuming. In this problem, we will see how text analytics can be used to automate the process of information retrieval.
CCQA A New Web-Scale Question Answering Dataset for Model Pre-Training
This open source chatbot project lets you create a chatbot that uses your own data to answer questions, thanks to the power of the OpenAI GPT-3.5 model.
Freelance Work - Website for a CBSE Coaching named SINGH TUTORIAL
keras implementation of cycle-gan
CNN implementation using tensorflow, keras to predict malariya using blood smears
This is a self made project to identify vandalism in wikipedia page
Here lies all the exercises I implement and share in my website
-Developed a supply chain network baseline MIP model for a glass manufacuterer with multiple products, manufacuting facilites, and production costs (Regular/Overtime) to find optimal product flow as per sourcing policies and capacity constraints. -To improve the service levels, developed a multi-objective MIP scenario model which finds the minmum number of warehouses to be built such that 80% of the demand is covered with in 500 miles of the nearest source. -Scenario model suggested to build 5 warehouses with their exact location and product flow information and was able to achieve reduction in transporatation cost by 19.75% with 80% demand served within 500 miles compared to 11% of demand within 500 miles in baseline model. -Coded in Python and performed optimization using Gurobi: pandas, dictionaries, loops, gurobi packages, csv package.
collaborate all your codes here
This is an E-learning website.
Implementation of Linear style transfer for sementics change
This is the Pytorch implementation of "Learning Linear Transformations for Fast Image and Video Style Transfer" (CVPR 2019).
All ML models applied from scratch will be uploaded here
Repository containing notebooks of my posts on Medium
Modin: Scale your Pandas workflows by changing a single line of code
Transformer (BERT, GPT2, etc.) based Training Module for popular NLP tasks