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
This project would tackle the problem of Image Super Resolution. Image super-resolution (SR) techniques reconstruct a higher-resolution image or sequence from the observed lower-resolution images. Often a low-resolution image is taken as an input and the same image is upscaled to a higher resolution, which is the output. The details in the high-resolution output are filled in where the details are essentially unknown. Super resolution is essentially what you see in films and series like CSI where someone zooms into an image and it improves in quality and the details just appear.
Why it is necessary?
There are many applications of super-resolution, it is used successfully for improving medical imaging systems, satellite imaging, in surveillance, astronomical imaging; new ideas are emerging all the time. Many Companies like Disney, Nvidia use this technique to increase the quality of Videos.
How are we going to do?
The traditional methods using interpolation causes blurriness in the image. Thus, we would focus on deep Learning methods. The solution we proposed includes usage of Generative Adversarial Networks (simply GANs) as a framework to tackle this problem
A good understanding of concepts of CS – 101 is always good. It would be preferable if you have an introduction to Python. Don’t worry if you are not very comfortable with Python. Please go through the small Python tutorial attached in the resources section. Along with this, interest to learn new things and enthusiasm is must.
Your selection would be based on your SOP and a small coding test in Python (If you are good with CS101 and have an intro to python, it should be a cake-walk)
This project would require good amount of commitment. But believe me, you would enjoy the journey and learn lots of Cool stuff.
- Introduction to the whole new world of machine Learning and Deep learning
- Master the Deep learning packages like Tensor Flow, keras
- Apply the hottest technique in Deep Learning – GANs
- Get acquainted to various techniques in image processing
- Last but not the least, apply your coding skills to build an entire Project from scratch
Python Tutorial - https://www.youtube.com/playlist?list=PL-osiE80TeTskrapNbzXhwoFUiLCjGgY7
Image Super Resolution - https://medium.com/beyondminds/an-introduction-to-super-resolution-using-deep-learning-f60aff9a499d
|Week Number||Tasks to be Completed|
|Week 1||Introduction to OpenCV and Learn Linear Regression and Logistic regression|
|Week 2||Get acquainted with Neural Networks and Deep learning|
|Week 3||Development of the concepts related to GAN and read, understand and implement a simple GAN model|
|Week 4-5||Read, understand and start implementing the GAN model of the Project using Tensor Flow|
|1||Complete tutorial on OpenCV|
|2||Introduction to ML and DL techniques|
|3||Complete tutorial on Resnet architecture|
|4||Complete tutorial on GAN|
|5||Read, learn and implement the Paper of SR|