ML Scholars Assignments

The assignments I completed while independently following the ML Scholars Program curriculum. It consists of coding assignments from the University of Michigan's EECS 498-007 course on Deep Learning for Computer Vision and a couple summaries of some basic ML papers.

Coding assignments (Google Colab)

• Pytorch 101
• K-Nearest Neighbor Classifier
• Linear Classifiers
• Two-Layer Linear Network
• Fully-Connected Linear Network
• Convolutional Neural Network

Paper Summaries

• Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization
• Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting