This is the repository for models done from scratch or direcly implemented in Machine Learning course during Fall 2023. It showcases various models and projects related to machine learning. Please feel free to email me at ruoheng.du@nyu.edu for any more information.
The /models directory contains models implemented from scratch, including KNN, K-means, Linear Regression, Logistic Regression, Lasso Regression, Gradient Boosting Decision Tree, Random Forests, and Back Propagation-based Multilayer Perceptron (MLP).
The /neural_networks directory includes the implementation of Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN) on the MNIST dataset. Additionally, it contains a report (ML_NN.pdf) summarizing and analyzing the performance of different models.
The /kaggle_cifar directory includes the code used in the final Kaggle Competition of the course. The competition involved using a modified CIFAR-10 model to achieve better classification results within 10 computational layers.
