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
- L.A.M.A. AI using Reinforcement Learning
- Intrusion Detection system
- Competitive coding
- Why The Hype Around GANs
- 3D reconstruction using 2D images
- Computer Vision Workbench
- 3D Object Classification using Mesh Neural Network
- Lossless high entropy compression algorithm
- ML GYM
- Tools for Web Development
- Strategy Wars [Online]
- Food Recommendation through Machine Learning
- Conversational Chatbot
- Virtual Keyboard
- Super Shenron
- Gestures for 3D space
- Road Network 3D Rendering using OpenGL
- Face Recognition using Statistics
- Introduction to Kaggle and Machine Learning
- Krittika Website
- Rubik's cube solver
- Planet/Atmosphere Renderer using OpenGL
- Digital Depth Perception
- KontaKt App
- Tinkerers’ Laboratory Website
- Graphic Intensive MUSIC APP
- Pool It!
- Insti Buddy
- Intelligent agents
Yann LeCun described GANs as “the most interesting idea in the last 10 years in Machine Learning”. And, indeed, Generative Adversarial Networks (GANs for short) have had a huge success since they were introduced in 2014 by Ian J. Goodfellow.
This project will involve learning many machine learning algorithms leading to GANs. Mentees will implement a Generative Adversarial Network from scratch.
For students who have participated in Summer of Science (Machine Learning track) before, this would be a great hands-on project!
|Week 1-2||Learn/Brush-up Python, Torch, Jupyter, Numpy, Unix commands|
|Week 3-4||Learn Linear Regression, Logistic Regression, Neural Networks|
|Week 5-6||Read up on the use cases and building blocks of Deep Learning.|
|Week 7-8||Implement a generative adversarial network from scratch and train it on toy dataset.|
|Week 9-10||Learn PyTorch/TensorFlow, implement a GAN network using the library.|
|Week 11-12||Start collecting data and training. Document all interesting observations|