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navneeeth/README.md

Hi! My name is Navneeth Krishna M

Full-Stack Software Engineer, Machine Learning Researcher, Adventurer

learning to code through the simulation

  • 🌍  I'm based in the United States
  • 🖥️  See my portfolio here
  • ✉️  You can contact me at navneeth.padaki15@gmail.com
  • 🚀  I'm currently working on FinGPT
  • 🧠  I'm learning Natural Language Processing
  • 🤝  I'm open to collaborating on Machine & Deep Learning projects
  • ⚡  Huge fan of Marvel's Daredevil!

Skills

C Java JavaScript Kotlin PHP Python HTML5 CSS3 JQuery Oracle MySQL Firebase Flask Heroku Figma

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  1. say-flight-prod say-flight-prod Public

    Say Flight is a Travel Insurance Booking Service for all your flight needs. The application is a full-stack web application built with HTML5, CSS, and JS on the front-end stack. On the back-end sta…

    HTML

  2. cloud-service-optimizer-prod cloud-service-optimizer-prod Public

    Cloud Service Optimizer is a web application that helps clients of cloud services optimize their services and instance types selection process. Created by Navneeth Krishna M, Nishchala M, and Oindr…

    Python

  3. youtube-video-summarizer youtube-video-summarizer Public

    Developed a YouTube Video Summarizer based on GPT-3.5-turbo and Whisper API by OpenAI. Validates YouTube link and OpenAI API Key, downloads the video, transcribes the video's audio, and summarizes …

    Python 1

  4. live-subtitle-generator live-subtitle-generator Public

    The Video Subtitle Generator is a project that combines the capabilities of PyAudio, opencv-python, OpenAI's Whisper API, and Deepgram API to generate subtitles for videos both uploaded and live.

    Python 9 3

  5. fin-gpt-prod fin-gpt-prod Public

    FinGPT is a full-stack application based on React.js, Python Flask, MongoDB, and gpt-3.5-turbo. It is an educational PoC finance app that provides various tools and analysis for managing investment…

    JavaScript 1 1

  6. instance-level-recognition-for-artworks instance-level-recognition-for-artworks Public

    Utilized Residual Neural Networks on classifying 220k+ classes of artworks from ‘The Met’ dataset. Developed and trained 10+ varieties of CNN backbones in a parametric approach alongside Contrastiv…

    Python