I'm currently an undergraduate, studying Computer Science at Ramaiah University of Applied Sciences. I am a researcher and my interest lies in the field of Machine Learning and Deep Learning. Computer Vision with Deep Learning is my favorite field to work with and this is the field where most of my works focus on. I am well versed with the technologies required today at the industry level. Python is my primary language to work with. I love open-source and I keep contributing to open-source technologies on a regular basis. Being a self-starter and a quick learner, I know how to do the best by making use of the best. Outside academics, I'm a National level Table Tennis Player and Freelance Software(Machine Learning) Developer and this comes from my passion for building stuff. I craft robust Machine Learning Models.
-Intern Under Professor Ganesh RamaKrishnan (Computer Science , IITB). -Research work on Optical Character recognition , Object Detection , Pose Detection . -Tools Used: Python , Tensorflow , Pytorch , R.
-Our team Worked on developing a Facial Recognition System . -Tools used- OpenCV , Python , Sciket Learn .
Google Code-in is a contest to introduce pre-university students (ages 13-17) to open source software development. Since 2010, over 8100 students from 107 countries have completed work in the contest.
Our Mission is to offer world class AI education to anyone on earth for free.
Welcome to the deeplearning.ai community! We’re a global, diverse network of deep learners passionate about learning and building AI. Our meetup series, Pie & AI, typically includes conversations with AI leaders, thought-provoking discussions, networking opportunities, hands-on project practice, and pie
Core Electives - Distributed System , Software Development , Enterprise Computing , Data Analysis , Machine Learning CGPA - 9.0 /10
Our Team have developed solution to segment different categories in a camera video feed which can be further used for different tasks . For self driving camera sensors on the vehicle takes the the images of surroundings and segement them to make decisions .
View ProjectAchieved State of the art Accuracy on the oxford pet dataset . We have used a pretrained model (Resnet-50 on Imagenet). Further we have used Techniques like Image Augmentation , Variable Learning Rate , Fine tuning .
View Project