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amazon-bedrock-samples

This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all availble foundational models

amazon-sagemaker-immersion-day

celmore25.github.io

clay-ds

data-science-on-aws

AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

dft_helpers

Python scripts for the use of helping writing and reading of Gaussian input output files. Extraction of dissociation energies and Raman Spectroscopy.

dmd-energy-markets

Dynamic Mode Decomposition (DMD) analysis of wholesale electricity markets

docker-airflow

Docker Apache Airflow

dynamic-pricing

Algorithmic pricing code

dynamic_mode_decomposition

This will hold all of the code I will be using in order to create a Python library which will allow me to perform Dynamic Mode Decomposition easily.

genai-llm-application-monitoring-on-aws

giskard

🐢 Open-Source Evaluation & Testing framework for LLMs and ML models

image_compression

This will be a small repo to show how to use SVD to compress an image.

langchain

⚡ Building applications with LLMs through composability ⚡

learninglog

linearalgebra

neural_net_predictions

Class project for Machine Learning that will use neural networks to forecast energy market prices.

non_linear_curve_fitting

Program used for non-linear Gaussian curve fitting as well as data extraction and wavelet decompositions of Raman signals. All data is property of the Schaefer Energy Research Laboratory (Notre Dame)

oop_in_python

This repo holds some basic files used to show how object oriented programming in Python can be a powerful tool!

portfolio

Personal ML/Statistics Portfolio

sagemaker-custom-project-templates

canary and linear MLOps deployment options

tabular-playground-kaggle

First kaggle competition repo

timewarpy

Time series pre and post processing methods intended to be native for deep learning

vocode-core

🤖 Build voice-based LLM agents. Modular + open source.

wavelet_ecg

This repository contains all of the code I developed for a machine-learning algorithm that uses wavelet signal processing to detect abnormal ECG signals. Results are in "Written Report". Objects are in "EKG_project_machine.py".