Course Description

Natural Language Processing is everywhere: machine translation, question answering, text summarization, dialogue systems—the applications are endless. Over the past decade, Deep Learning has revolutionized NLP, becoming the go-to solution for a wide range of problems.

This course is designed to provide a comprehensive understanding of various Deep Learning architectures and their applications in NLP. We’ll dive into core ideas such as:

• Learning representations at various levels

• Training and fine-tuning models,

• Techniques like in-context learning, parameter-efficient fine-tuning, and

• Advanced topics include Reinforcement Learning with Human Feedback (RLHF) and other alignment techniques such as Direct Preference Optimisation (DPO). The course combines theoretical insights with practical learning.
  
  

• Please find the lectures here 

• Please find the Notebooks in the Tutorials section of the website, in the top-right corner.