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Hi 👋, I'm Aby Stalin

AI / ML Enthusiast | Secretary of @ACM-Amrita-Amritapuri | B.Tech A.I. Student at Amrita Vishwa Vidyapeetham, Amritapuri

alby0n

alby_0n

  • 💬 Ask me about Machine Learning

  • 📫 How to reach me [email protected]

  • ⚡ Fun fact I think I am funny and very friendly

Connect with me:

alby_0n aby-stalin albyon alby.on u4aie20104_dsa2

Languages and Tools:

cplusplus java linux matlab opencv pandas photoshop python scikit_learn tensorflow

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Aby Stalin's Projects

hacktoberfest icon hacktoberfest

Participate in Hacktoberfest by contributing to any Open Source project on GitHub! Here is a starter project for first time contributors. #hacktoberfest

hacktoberfest2022-1 icon hacktoberfest2022-1

Make your first Pull Request on Hacktoberfest 2022. Don't forget to spread love and if you like give us a ⭐️

mapbox-ar-pathfinder icon mapbox-ar-pathfinder

Augmented Reality-Based Location Discovery: Integration of Unity IDE, Mapbox SDK, and ARcore Framework for Seamless Navigation

masked-face-recognition icon masked-face-recognition

Identify subjects from CCTV footages even when there are only limited number of photographs available for the suspected individuals. The system should work in near real time and even when there are partial occlusion, different pose, and physical changes like beard , eye-glasses and face masks .

program-collection icon program-collection

Anyone can contribute to the Program Collection Repo. Please Share valuable Codes which can help others 💖 Don't forget to spread love and if you like give me a ⭐️

rosberrypi-chess-bot icon rosberrypi-chess-bot

In this project we aim to create a chess bot that provides us with the best possible move to play against a real world opponent, using a Raspberry Pi along with ROS 1 (Noetic) to provide communication between the camera and the open source chess engine

spam-email-classifer-matlab icon spam-email-classifer-matlab

Naive bayes theorem is applied assuming that each word is strongly independent and doesn't depend on occurrences of other words. • We find the posterior probability of each word given to test, based on the previously known probabilities and prior. • Prior is the chance whether an arbitrary mail is spam or non-spam, based on how frequently we receive them.

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