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
In this project, mentees will learn about the four of the famous Convolutional Neural Network (CNN) architectures - AlexNet, VGGNet, ResNet and GoogleNet.
No. of mentees: 5-6
All of these are classic deep neural networks that have performed exceptionally well in the ImageNet Challenge (ILSVRC) in different years and have functioned as backbones for many computer vision tasks.
The project will involve learning about fundamental concepts and algorithms used in machine learning to analyze the salient features of these different architectures and evaluate cases where these models outperform others. After a basic implementation of these architectures using PyTorch, students would move onto use transfer learning to train deeper models on bigger datasets for classification and localization.
Have a look at the case study presented in these slides - http://cs231n.stanford.edu/slides/2019/cs231n_2019_lecture09.pdf
Bonus points for comparing performance in a more complex CV task such as face or gesture recognition!
This project is ideal for students who want to get started with Deep Learning for Computer Vision.
Tentative Project Timeline
|Week Number||Tasks to be Completed|
|Week 1||Learn/Brush-up Python, PyTorch, Jupyter, Numpy, Unix commands|
|Week 2||Learn about logistic regression, activation function, gradient descent and neural networks|
|Week 3||Learn about CNNs for image classification and give a read to the architectures presented in the original papers of these models|
|Week 4||Implement the models from scratch for classification and compare performance|
|Week 5||Read about Transfer Learning to train models for more complex tasks and Document results|