Behind the Secrets of Large Language Models
This course provides a practical and in-depth understanding of large language models that power modern natural language processing systems. Students will explore the architecture, training methodologies, capabilities, and ethical implications of LLMs. The course combines theoretical knowledge with hands-on experience to equip students with the skills necessary to develop, analyze, and apply LLMs in various contexts.
- Lecture -- Mondays, 11:10 - 12:40 at FOE/0244/H
- Exercise -- Mondays, 16:40 - 18:10 at FOE/0244/H
By the end of this course, students will be able to:
- Understand the architecture and key components of large language models.
- Analyze the training processes, including data collection, model optimization, and fine-tuning.
- Evaluate the performance and limitations of LLMs in different NLP tasks.
- Apply LLMs to real-world problems, such as text generation, summarization, and translation.
- Discuss the ethical considerations and societal impacts of deploying LLMs
Prerequisites
- Introduction to Machine Learning or equivalent
- Basic understanding of neural networks
- Programming experience in Python
This course provides an in-depth understanding of large language models (LLMs) that power modern natural language processing (NLP) systems. Students will explore the architecture, training methodologies, capabilities, and ethical implications of LLMs. The course combines theoretical knowledge with hands-on experience to equip students with the skills necessary to develop, analyze, and apply LLMs in various contexts.