Large Language Models

Catalog description

This course delves into the theory, algorithms, and applications of large learning models, with a particular focus on deep learning and transformer-based architectures. Students will explore state-of-the-art techniques for training, optimizing, and deploying large-scale models in a variety of domains, including natural language processing, computer vision, and reinforcement learning. The course will cover model architecture design, distributed training strategies, and the computational challenges associated with large model scalability.  In addition to foundational principles, the course will address emerging trends in model interpretability, ethical concerns, and the impact of large learning models on society. Students will gain hands-on experience through practical assignments and projects that involve building and fine-tuning large learning models using modern tools and frameworks.