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|