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 Running Projects
- Browser Based PDF manager
- Resume Script Generator
- Physicc : A Simple Physics Engine
- Image Colorization
- Language Model Based Syntax Autocompletion in a Text Editor
- Computer vision based web app
- Cribbit Cribbit (Open for PGs Only)
- Techster Texter
- Language Detection
- Book Tracker
- ResoBin - Not the bin we deserve but the bin we need!
- Agree to disagree
- Watson (World's smartest assistant in your pocket)
- Meta Learning - Learning to Learn
- Break free of the matrix, by building one!
- Procedurally Generated Infinite Open World
- Introduction to App Development
- PAC MAN
- Introduction to Web Development
- Goal ICPC
- Traffic congestion modelling and rendering
- Tools for Data Science
- Machine Learning Based Metropolitan Air Pollution Estimation
- Audio controlled drone
- NLPlay with Transformers
- DIY FaceApp
- A Deep Dive into CNNs
- Competitive Coding
- Snake AI
- Facial Recognition App
- Gaming meets AI !!!
- R(ea)L Trader
- Computational Geometry
- Deep reinforcement learning - 2048 AI
- Reinforcement Learning to Finance
- Developing Hybrid ANN-Statistical Model for Robust Stock Market Prediction
- Astronomical Data-modelling and Interpretation
- Visual Perception for Self Driving Cars
- Convolutional Neural Networks and Applications
- Quantum Computing Algorithms
- Algorithm Visualizer
- Anime Club IITB Website using Django
- Machine Learning in Browser
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.