WnCC - Seasons of Code
Seasons of Code is a programme launched by WnCC along the lines of the Google Summer of Code. It provides one with an opprtunity to learn and participate in a variety of interesting projects under the mentorship of the very best in our institute.
List of Projects
- KontaKt App
- Sudoku Spoiler
- GLSL Raytracing
- Decentralized Land Registration on Ethereum
- Galactic Collision Simulator
- Competetive coding
- The Unreasonable Effectiveness of Recurrent Neural Networks
- Generating a human pose dataset using PC games
- Monte Carlo Path Tracing Renderer
- Tabbing App
- Geo-location Augmented Reality
- AR chess app
- Prevention of Sophisticated DoS attack / Network Security
- Winning a Deep Learning challenge
- Automated Fiducial Localisation from MRI/CT Images
- Panorama in Cam Scanner
- 3D Object Reconstruction from Single Image
- Statistical Modelling of Star Maps
- Face Recognition Systems
- Competitive Coding
- Can Machines Identify Genres?
- Joint Modelling of Source Code and Natural Language
- Front end development for FOSSEE websites
- Institute Delivery System
- Capture The Swag
- Panorama in Cam-Scanner
- Poisson Solver with Image Editing
- Blind Source Separation
- FAQ Bot for Freshmen
- Capturing semantic structures in Neural Machine Translation
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.
Again implementation in python using opencv and must follow blockwise execution and tutorial format.
- Read up on the Adaboost algorithm.
- Study mathematical theory.
- Start implementation of basic Adaboost algorithm in Python.
- Finish code for Adaboost in Python.
- Vectorise the code using numpy. Speed up of about 10x.
- Read up on digit recognition via Adaboost.
- Begin writing code for digit recognition.
- Write Ipython notebook for Adaboost.
- Acquire training and testing data(images and labels).
- Complete digit classification code.
- Test the digit classifier on data.
- Debug and optimise output and successfully implement the digit classifier.
- Add digit classification to the Ipython notebook.
The project was completed in a little less than three weeks. The repo for the project can be found here. The rendered iPython notebook can be found at the official iPython Wiki or can be directly accessed here.