Deep Learning neural network models have been successfully applied to natural language processing, and are now changing radically how we interact with machines (Siri, Alexa, machine translation or the Google search engine). Large Language Models are at the core of these developments, and are being used to crack "languages" in other disciplines, ranging from programming languages (Copilot) to proteins (AlphaFold) and gene sequences (GenSLM).
This course introduces in detail the machinery that makes Deep Learning work for NLP, including the latest Transformers and Large Language Models like GPT, BERT and T5. It also covers the use of prompts for zero-shot and few-shot learning, as well as multimodal text-image models like GPT-4. The course combines theoretical and practical hands-on classes. Attendants will be able to understand the internal working of the models, and implement them nearly from scratch in Tensorflow. The aim is to allow attendees to acquire the ability to understand, modify and apply current and future Deep Learning models to NLP and other areas.
NOTE on online attendance: In addition to onsite attendance, the classes will be broadcasted live online. The practical labs are also available online, with split groups with one lecturer in each. We offer a high-quality and engaging course, both at the theoretical and hands-on practical sessions.Addressed to professionals, researchers and students who want to understand and apply deep learning techniques to text. The practical part requires basic programming experience, a university-level course in computer science and experience in Python. Basic math skills (algebra or pre-calculus) are also needed.