Skip to content

Varunv003/Varunv003

Folders and files

NameName
Last commit message
Last commit date

Latest commit

Β 

History

13 Commits
Β 
Β 

Repository files navigation

Hi there, I am Varun Vij πŸ‘‹

πŸ’« About Me:

  • πŸ€– I am a passionate software engineer with a strong foundation in AI, machine learning, and full-stack development. I love building innovative solutions that solve real-world problems.

  • πŸ”₯ I have experience working on projects involving AI-powered document retrieval, content generation platforms, and backend development for live sports data.

  • 🌱 Currently, I am diving deeper into AI, exploring Generative AI, document retrieval systems, and full-stack development.

  • πŸ“ Check out my blog: Blog Badge

  • 🎯 Portfolio website: Badge

  • 🌐 Let's Connect: Linkedin Badge Gmail Badge


πŸ’» What I do:

  • AI and Machine Learning – Developing models using Python, TensorFlow, and Scikit-learn for real-world applications.
  • Full-Stack Development – Building responsive web apps using Next.js, React, and Spring Boot.
  • Backend Development – Creating efficient APIs and handling data storage using MySQL and NEON PostgreSQL.
  • AI-Powered Systems – Leveraging AI for document querying and content generation.

πŸ›  Tech Stack:

Python C++ Java JavaScript React Django Jinja TensorFlow Keras FAISS Streamlit Next JS Spring TailwindCSS Postgres MySQL MongoDB Vercel GitHub Git


πŸ”₯ Projects:

  • DocChat – Langchain Retrieval System

    • AI-powered chatbot for querying PDF documents in real time.
    • Built using Langchain, FAISS, and Google Palm Embeddings with a user-friendly interface for managing document queries.
  • AI Content Generator – AI-powered platform for dynamic content generation.

    • Developed using Next.js, NEON PostgreSQL, and Drizzle ORM for seamless content creation.
  • Cricket Score API Backend – RESTful API for live cricket scores.

    • Built with Spring Boot and MySQL for real-time sports data.
  • FloraVision – Deep learning model for plant species recognition.

    • Achieved 95% accuracy using CNNs and transfer learning on a diverse plant species dataset.

About

Config files for my GitHub profile.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published