| Courses 2025 |
Deep
Learning for NLP (code: DL4NLP)
September 13th to 19th, 20 hours, 5 afternoons. 14th
edition.
Instructor:
Eneko Agirre
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. Attendants will be able to understand, modify and
apply current and future Deep Learning models. They will
learn the inner workings of the models and implement them
in Keras.
Student profile:
professionals, researchers and students with basic
programming and Python experience. Basic math skills
(algebra or pre-calculus) are also needed. Although not
strictly necessary, we recommend subscribing to Collab Pro
for more out of GPUs.
Cost: 270€ (+ 4€ insurance, you only need to pay
the insurance for one of the courses)
|
Large
Language Models (code: LLM)
September 29th to October 03th, 20 hours, 5 afternoons.
2nd edition.
Instructor:
Oier Lopez de Lacalle
The course will introduce large language models, with
special emphasis on adaptation techniques (e.g. in-context
learning, few-shot, instruction learning) and ways to
align with human preferences. In addition, advanced
training techniques such as parallelism, selective
architectures and scaling laws are presented.
Participants, in addition to understanding the
fundamentals of LLMs and learning advanced training
techniques, will gain hands-on experience in applying and
working with these models, while addressing biases and
ethical concerns.
Student profile:
professionals, researchers and students with basic
programming and Python experience. Basic math skills
(algebra or pre-calculus) are also needed. Although not
strictly necessary, we recommend subscribing to Collab Pro
for more out of GPUs.
Cost: 270€ (+ 4€ insurance, you only need to pay
the insurance for one of the courses)
|
Introduction
to LT Applications (code: ILTAPP)
October 13th to 17th, 20 hours, 5 afternoons. 8th
edition.
Instructor:
Rodrigo Agerri
This course will introduce the most commonly used
techniques to build applications based on Language
Technology.
Attendees will learn how to apply techniques such as
document classification, sequence labeling, as well as
vector-based word representations (embeddings) and
pretrained language models for core applications such as
Opinion Mining, Fake News Detection or Question Answering.
Student profile: graduate
students and professionals from a range of disciplines
(linguistics, journalism, computer science, sociology,
etc.) that need an applied introduction to Language
Technology. For the practical content (coding exercises)
some experience in python programming is recommended.
Although not strictly necessary, we recommend subscribing
to Collab Pro for more out of GPUs.
Cost: 270€ (+ 4€ insurance, you only need to pay
the insurance for one of the courses)
|
Generative
Playground: LLMs made easy (code: GPLLMME)
October 27th to 31th, 20 hours, 5 afternoons. 2nd
edition.
Instructor:
Ander Barrena
The aim of this course is to understand and deploy large
language models (LLMs) from a practical perspective,
enabling students to gain hands-on experience with these
models without coding, with particular emphasis on ethical
considerations, including addressing bias in language,
responsibly handling sensitive information, and evaluating
the deployed models.
Participants will learn how to use proprietary models like
GPT-4o and open-source models like LLaMa3 for prompt
engineering, creating agents, chatbots, Retrieval
Augmented Generation (RAG) models, and other NLP
applications.
Student profile: graduate
students and professionals from various disciplines
(linguistics, journalism, computer science, sociology,
etc.) who need to understand and deploy LLMs easily. No
coding skills are necessary for the practical content.
Although not strictly necessary, the OpenAI ChatGPT Plus
subscription plan is advisable to complete some of the
labs.
Cost: 270€ (+ 4€ insurance, you only need to pay
the insurance for one of the courses)
|
