Pytorch implementation of λOpt: Learn to Regularize Recommender Models in Finer Levels, KDD 2019

This is Collection of Regularization Deep learning techniques with code and paper

This repo contains the experimentation of the most important deep learning techniques related to Regularization, Optimization and HPO while using LeNet CNN architecture as the playground.

This project explores ML techniques across classification and regression. It includes penguin species classification, breast cancer prediction, and baseball performance prediction using regularization. After, I will develop an XGBoost model for hotel cancellation prediction, analyzing key booking factors and optimizing performance. (In Progress)

Deep Learning project about the design and training of a model for Image Classification

Salary Prediction Classification:

This is an expansion of dsb318-group4 (see repo: dsb318-group4), in which we collaborated to predict high school graduation rates in CA from other trends (e.g., poverty rate, availability of e-cigarettes). Collaboration between Eli and Emily.

This is a classification model implementation using Random Forest and Logistic Regression in Python and Spark. Originally implemented via AWS EMR Clusters.

Machine Learning End-to-End (Linear Regression) model project on MS Application Prediction

An effective way to avoid (or at least to reduce) overfitt

An online course on ML taught by Andrew Ng. Introduces algorithms from scratch including regression models, classification, Neural Networks, SVMs, K-Means clustering, and applications such as Photo OCR.

This project explores ML techniques across classification and regression. It includes penguin species classification, breast cancer prediction, and baseball performance prediction using regularization. After, I will develop an XGBoost model for hotel cancellation prediction, analyzing key booking factors and optimizing performance. (In Progress)