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
This project will involve learning many machine learning algorithms leading to RNNs. Mentees will implement a Neural Network and a Recurrent Neural Network framework from scratch
“Almost 4 years ago, Karpathy published a blog post(http://karpathy.github.io/2015/05/21/rnn-effectiveness/) that has since become quite well known in the community. Karpathy discusses some awesome results he achieved by training character level RNN on various text corpus. Anybody interested in this project is expected to go through the post thoroughly, even if you can’t understand most of it. We aim to follow Karpathy’s approach to understand and gain a deeper appreciation of RNNs while also exploring their versatility.
This project will involve learning many machine learning algorithms leading to RNNs. Mentees will implement a Neural Network and a Recurrent Neural Network framework from scratch. We will attempt to reproduce Karpathy’s results and go beyond to training on more data like Obama’s speeches, Trump’s tweets, the Bible, turtlesim code, cooking recipes, MIDI sequences, etc.
For students who have participated in Summer of Science(Machine Learning track) before, this would be a great hands-on project!
Write about your prior experience with things mentioned in the prerequisites and a list any prior machine learning projects completed. Do send across links to your project repos and demos, if any, along with the proposal. Although this is not mandatory but try to include a rough expected timeline for yourself.
The following points must be included in the proposal for the project:
- Your motivation and understanding of the project
- Background in ML/DL (include your previous projects)
- How do you want to approach the problem, you thoughts/remarks.
- Experience with Python and scientific libraries
Timeline for each week :
- Finish Linear Algebra, Vector Calculus, and Statistics refresher
- Install Ubuntu, set up a development environment
- Learn/Brush-up Python, Torch, Jupyter, Numpy, Unix commands
- Learn Linear Regression, Logistic Regression, Neural Networks
- Read up on the use cases and building blocks of Deep Learning
- Implement a recurrent neural network from scratch and train it on toy dataset.
- Learn PyTorch, implement an RNN/LSTM network using PyTorch.
- Start collecting data and training
- Document all interesting observations