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
The project will be a implementation of the paper A Point Set Generation Network for 3D Object Reconstruction from a Single Image
The code is available at: https://github.com/fanhqme/PointSetGeneration. Since the code is done using Tensorflow, this will try to replicate the results using Pytorch. A bonus part of the project would be to try to remove a major constraint imposed by the author.
Hard prerequisite: Prior experience in Python coding. Everything else can be learned on the fly if the student is motivated enough. A course in Probability Theory is also very much recommended.
Soft Pre-requisite: Prior experience in Pytorch or any other deep learning library (can be learnt on the go if mentee is motivated). Basic 3D vision concepts.
|Week 1-2 :||Cover up existing pre-requisites, thorough reading of the paper, and a discussion among the group and me. Getting a template project running.|
|Week 3-4 :||Code parts of the algorithm and check each part individually if they are training.|
|Week 5-7 :||Hyperparameter tuning and debugging the models. This is expected to take the major chunk of time. The bonus part will be done only if this part is completed within 1.5 weeks.|
|Week 8 :||Documenting everything that has been done properly (note documentation will be an ongoing process, this will just be wrapping up everything).|
Expected amount of hours mentees need to spend: In short depends. Coding in a new library can often be daunting and it may require more than one nightouts. Average 10-15 hours in a week, might be higher in some particular cases.
Resources required: Google GPUs will be used for this purpose. Google gives $300 credits for first time users.