Seasons of Code is a programme launched by the WnCC, along the lines of GSoC without much greenery though. The incentive is similar to ITSP, based on the current form of it, the fundamental difference is that one can choose from the ideas offered by mentors who are senior undergrads, doctorate students or professors, and in some exceptional cases, startups. We plan to have a really long timeframe though, until the next winter extending this programme into a mentorship of sorts into the semester. It is not just about development by the way. We have some mentors ready to take up programmes regarding competitive coding and scientific computation too.
Seasons of Code gives you an amazing opportunity to learn and dive into coding under the mentorship of the best in our institute. Our list of projects gives you a chance to pick up and work on any topic you are enthusiastic about.
Do. Or do not. There is no try.
The Force is strong with you. Train yourself to let go of everything you fear to lose. The Force will be with you always. Ready are you?
I can feel you code. It gives you focus. It makes you stronger. Your focus determines your reality. Use the force and someday you will be the most powerful Jedi ever.
Become a MasterYour eyes can deceive you. Don’t trust them.
This internship involves the implementation of a Monte Carlo Path Tracer.
This internship involves development of a web tool for rapidly creating and editing dynamic playlists of YouTube music videos.
Building and Setting up dwn on any OS, and creating a patch to add animation support to it.
Each one of us must have used the Cam-scanner app on Android phones for quick and good quality scanning of documents. But what if you have to scan a really big document? Or maybe you want to capture more detail?
This notebook will be written using python, also employing numpy and OpenCV, we will initially implement Poisson solver for the discrete case which is immensely useful for many application then we will proceed on the application part which will be from this paper
Boosting is a well known machine learning technique, we use simple weak classifiers in cascade fashion to form a strong classifier. It’s extremely effective, facebook uses some version of this algorithm for detecting faces (99.9% accurate). Implement basic adaboost on simulated data, then for digit recognition.
The project involves exploring various implementations of Independent Component Analysis on sound/images and demonstrating through an ipython notebook.
This internship involves front-end development for various FOSSEE websites.