top of page
CertificateBackground.png

Certificate of Completion

ErdosHorizontal.png

THIS ACKNOWLEDGES THAT

HAS COMPLETED THE MAY-SUMMER 2024 DATA SCIENCE BOOT CAMP

Ming Zhang

clear.png

Roman Holowinsky, PhD

JUNE 10, 2024

DIRECTOR

DATE

TEAM

QED

Cisil Karaguzel, Ming Zhang, Hatice Mutlu, Adnan Cihan Cakar, Matthew Gelvin

clear.png

The state-of-the-art language models have achieved human-level performance on many tasks but still face significant challenges in multi-step mathematical reasoning. Recent advancements in large language models (LLMs) have demonstrated exceptional capabilities across diverse tasks, including common-sense reasoning, question answering, and summarization. However, they struggle with tasks requiring quantitative reasoning, such as solving complex mathematical problems. Mathematics serves as a valuable testbed in machine learning for problem-solving abilities, highlighting the need for more robust models capable of multi-step reasoning. The primary goal of this project is to develop a customized LLM that can provide step-by-step solutions to math problems by fine-tuning a base LLM using a large mathematical dataset.

Screen Shot 2022-06-03 at 11.31.35 AM.png
github URL
bottom of page