Large Language Models Methods and Applications (11-667) is a graduate-level course that aims to provide a holistic view of the current state of large language ...

Three homework assignments (30%). • To be completed Individually. • Mixture of practical and comprehension-based questions. • To be turned in via Canvas.

This course provides a broad foundation for understanding, working with, and adapting existing tools and technologies in the area of Large Language Models.

CMU 11-667 Course Project. Page 73. Background. • Research Question: How to let model confidence reflect model performance. • Why Bother? Obtaining calibrated ...

This repo contains draft course materials for On-Device Machine Learning 11-767 at CMU. Official content is availble at cmu-odml.github.io

11667 focuses on models, learning algorithms and applications. 11868 focuses on building systems for LLM, including training, serving, and maintaining. System ...

5 нояб. 2024 г. · We're on a journey to advance and democratize artificial intelligence through open source and open science.

We're on a journey to advance and democratize artificial intelligence through open source and open science.

Lecture 1.1 - Introduction (CMU Multimodal Machine Learning course, Fall 2022) · Lecture 1.2 - Multimodal Research Tasks (CMU Multimodal Machine Learning course, ...

Github Repo for 11-667 at CMU. This is the website repository for the Fall 2023 iteration of 11-667: Large Language Models Methods and Applications.