normalization-techniques

[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

Official implementation for "Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images" https://arxiv.org/abs/2112.08810

This project analyzes e-commerce order fulfillment using Advanced SQL Techniques and Python-based visualization to uncover insights on sales trends, customer segmentation, shipping cost optimization, and payment preferences.

Learning about the algorithms used in machine learning, along with techniques for training and testing models.

Continuous Norming with R

Evaluating the Robustness of Biomedical Concept Normalization

Implementation of some famous data mining algorithms in C++11 boost

Analyze the customer data, build a neural network to help the operations team identify the customers that are more likely to churn, and provide recommendations on how to retain such customers

The projects are part of the graduate-level course CSE-560 : Data Models and Query Language [Fall 2019 @ UB_SUNY] ... Course Instructor : Jan Chomicki (https://cse.buffalo.edu/~chomicki/)

The goal of this project is to develop an automatic classification system for exotic fruits based on numerical features using the K-nearest neighbors (K-NN) algorithm