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Hi šŸ‘‹, I'm Atharv

I am Final Year Undergrad in the Department of Electrical Engineering at IIT Hyderabad .I am interested in Deep-Learning. I am new to the field and am currently exploring Self-Supervised Learning in Images. I am also interested in Natural Language Processing -Specifically - RAG, Vector Databases. Feel free to reach out to me if you have an interesting project idea or want to work with me

  • šŸŒ± Iā€™m currently learning about Retrieval Augmented Generations and Vector Databases

  • šŸ‘Æ Iā€™m looking to collaborate on Deep Learning based research projects

  • šŸ’¬ Ask me about Deep Learning/Robotics/Embedded Systems

  • šŸ“« How to reach me [email protected]

  • āš” Fun fact I like to play Field Hockey

  • Academic CV

Connect with me:

atharv-ramesh

Languages and Tools:

Expert:

python pytorch arduino c cplusplus git linux

Intermediate:

matlab html5 tensorflow opencv pandas scikit_learn

Beginner:

css3 javascript react seaborn

atharvrn

Ā atharvrn

atharvrn

Atharv Ramesh's Projects

code-samples-vit icon code-samples-vit

Holds code for our CVPR'23 tutorial: All Things ViTs: Understanding and Interpreting Attention in Vision.

deep-learning-based-channel-estimation-schemes-for-ieee-802.11p-standard icon deep-learning-based-channel-estimation-schemes-for-ieee-802.11p-standard

This repository includes the source code of the STA-DNN and TRFI DNN channel estimators proposed in "Deep Learning Based Channel Estimation Schemes for IEEE 802.11 p Standard" and "Joint TRFI and Deep Learning for Vehicular Channel Estimation" papers that are published in the IEEE Access journal and the proceedings of the 2020 IEEE GLOBECOM Worksh

drone_control icon drone_control

Official Submission for Drona Aviation PS at Inter IIT Tech Meet 11.0

pysimplex icon pysimplex

A Python based API, built atop Pymodbus to control SimplexMotion Servo motors

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

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