Lionel Hutz is my favorite simpsons character

Greg Schreiter

Python Developer

Machine Learning & Data Science

My Work in AI/ML/DS

I'm a Python programmer passionate about ensuring the quality and reliability of AI systems. At dataannotation.tech, I evaluate and refine cutting-edge AI models across a diverse range of projects. My work focuses on critically assessing AI-generated outputs, including code, algorithms, games, simulations, and general chatbot interactions, to ensure they meet the highest standards of correctness, groundedness and performance. When the tasks are more open-ended, I try to focus mostly on general ML/DL/AI topics.


Key Contributions

MNIST Digit Recognizer

Draw a single digit (0-9) below.

Draw Here:

Prediction:

Draw a digit and click Predict.

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Gradient Descent

Linear Regression Fit

Gradient Descent Animation

Medium Articles

Selected Projects

Project 1 Screenshot

U of M Bootcamp Final Project

I helped develop a machine learning model to predict diabetes diagnoses with up to 97% accuracy. This model analyzes patient health metrics such as gender, age, BMI, and blood glucose levels to identify potential diabetes cases. My primary contributions included building and evaluating Support Vector Machine (SVM), deep learning, and advanced decision tree models.

GitHub Repo
Project 2 Screenshot

Hands-on Machine Learning (3e) Code Replication

I completed reconstructing the notebooks for the main text of the book Hands-on Machine Learning (3e) by Aurélien Géron in April 2025. This book provides a comprehensive overview of modern machine learning methods using Python, with a special focus on convolutional and recurrent neural network architectures, as well as general deep and machine learning best practices.

GitHub Repo
Project 3 Screenshot

Medical Image Classification with Convolutional Neural Networks

Collaboration with Christine Jauregui

We built a convolutional neural network (CNN) to classify chest CT scans for cancer detection. We trained a CNN from scratch and then explored the benefits of transfer learning by leveraging pre-trained models to improve performance. Additionally, we investigated the potential benefits and drawbacks of using model ensembles.

GitHub Repo