Streamlit app to illustrate University of Arizona public salary data

Notes and description of the Project Data Science Lab

Project #2

Docker base container definition for PHP-CLI applications

A work-in-progress utility R-package with consolidated tools and templates prepared for University of Arizona's College of Applied Science and Technology. 🐻⬇️

Standards and Best Practices for UA Graduate College Developers

INFO 527 — Neural Networks: Assignment 2 (Backpropagation). Implementation of a feedforward neural network with sigmoid activation and training via backpropagation using gradient descent. Includes gradient computation, prediction, and pytest validation. Part of the Master’s in MIS/ML program at the University of Arizona.

Docker container definition for the Linux (Ubuntu) - Apache - PHP components of a LAMP stack application

INFO 527 — Neural Networks: Assignment 3 (Feedforward Neural Networks). Implementation of deep, wide, and regularized feedforward neural networks using TensorFlow Keras. Includes experiments with dropout and early stopping, tested via pytest for performance verification. Part of the Master’s in MIS/ML program at the University of Arizona.

INFO 527 — Neural Networks: Assignment 4. Implementation of deep learning architectures using TensorFlow Keras, including convolutional neural networks (CNNs) for image and text classification and recurrent neural networks (RNNs) for sequence modeling. Part of the Master’s in MIS/ML program at the University of Arizona.

Geochronology tool for identifying laser ablations across Mass-Spectrometer voltage integrations via an interactive visual plot

A Docker build file for the unoconv-node base image

INFO 527 — Neural Networks: Assignment 1 (String-to-Vectors). Implementation of an Index class for mapping strings to numeric vectors and matrices, tested using pytest. Part of the Master’s in MIS/ML program at the University of Arizona.

🧠 Implement convolutional and recurrent neural networks in TensorFlow Keras to handle image and text datasets, enhancing model performance through advanced techniques.