Ved Prakash's Projects
Extracting addresses from text
The project is based on a multi-label classification problem in NLP.
In online advertising, click-through rate (CTR) is a very important metric for evaluating ad performance.
A curated list of analytics frameworks, software and other tools.
This repository contains tips and resources to prepare for behavioral interviews.
A curated list of Generative Recommender Systems (Paper & Code)
This repository contains System Design resources which are useful while preparing for interviews and learning Distributed Systems
Go ahead and axolotl questions
This a again a sample repository.
Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.
This is a sample for creating Github and connect local development.
This ia again anew demonstration for github.
This is a sample repository for updating work through github online.
This application is a car insurance recommender system.
An NSFW Image Classifier including an Automation Engine for fast deletion & moderation built with Node.js, TensorFlow, and Parse Server
Code for "Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation" (NeurIPS 2019)
This Repository is for C++ Interview Practice
This is a portfolio project for students
codes collection for ML/DL/RL/BigData projects
This is Data Structure and Algorithm with C++
ใPyTorchใEasy-to-use,Modular and Extendible package of deep-learning based CTR models.
This repository is for Diabetes Readmittance Prediction.
A ready-to-run Docker container setup to quickly provide MLflow as a service, with PostgreSQL, AWS S3, and NGINX
LLM powered Physics docQA
My solution to the book A Collection of Data Science Take-Home Challenges
๐ ๐ง๐ต๐ฒ ๐๐๐น๐น ๐ฆ๐๐ฎ๐ฐ๐ธ ๐ณ-๐ฆ๐๐ฒ๐ฝ๐ ๐ ๐๐ข๐ฝ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ | ๐๐ฒ๐ฎ๐ฟ๐ป ๐ ๐๐ & ๐ ๐๐ข๐ฝ๐ for free by designing, building and deploying an end-to-end ML batch system ~ ๐ด๐ฐ๐ถ๐ณ๐ค๐ฆ ๐ค๐ฐ๐ฅ๐ฆ + 2.5 ๐ฉ๐ฐ๐ถ๐ณ๐ด ๐ฐ๐ง ๐ณ๐ฆ๐ข๐ฅ๐ช๐ฏ๐จ & ๐ท๐ช๐ฅ๐ฆ๐ฐ ๐ฎ๐ข๐ต๐ฆ๐ณ๐ช๐ข๐ญ๐ด
๐ช End-to-end NLP workflows from prototype to production