Deep Learning neural network models have been successfully applied to natural language processing, and are now changing radically how we interact with machines (Siri, Amazon Alexa, Google Home, Skype translator, Google Translate, or the Google search engine). These models are able to infer a continuous representation for words and sentences, and generalize to new tasks with much less training data. The seminar will introduce the main deep learning models used in natural language processing, allowing the attendees to gain hands-on understanding and implementation of them in Keras.

This course is a 20 hour introduction to the main deep learning models used in text processing, covering the latest developments, including Transformers and pre-trained (multilingual) language models like GPT, T5, BERT, and their use with fine-tuning and prompting. It combines theoretical and practical hands-on classes. Attendants will be able to understand and implement the models in Keras.

The course is part of the NLP master hosted by the Ixa NLP research group at the HiTZ research center of the University of the Basque Country (UPV/EHU).

Student profile

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.


Introduction to machine learning and NLP with Keras

Machine learning, Deep learning
Natural Language Processing
A sample NLP task with ML
. Sentiment analysis
. Features
. Logistic Regression
LABORATORY: Sentiment analysis with logistic regression

Multilayer Perceptron and Word Embeddings

Multiple layers ~ Deep: MLP
Backpropagation and gradients
Learning rate
More regularization
Representation learning
Word embeddings
LABORATORY: Sentiment analysis with Multilayer Perceptron

Recurrent Neural Networks, Seq2seq, Neural Machine Translation

From words to sequences: RNNs
. Language Models (sentence encoders)
. Language Generation (sentence decoders)
. Sequence to sequence models and Neural Machine Translation (I)
Better RNNs: LSTM
LABORATORY: Sentiment analysis with LSTMs

Attention, Better Machine Translation and Natural Language Inference

Re-thinking seq2seq:
. Attention, memory, Transformers
. State of the art NMT
Natural Language Inference with siamese networks
LABORATORY: Attention Model for NLI

Pre-trained Transformers, BERT, GPT.

Pre-trained language models.
. Multilingual transfer learning
. Fine-tuning, Prompting
Deep learning frameworks
Last words
LABORATORY: Pre-trained transformers for sentiment analysis and NLI


Person 1

Eneko Agirre

Professor, member of IXA
Director of HiTZ

Person 2

Oier Lopez de la Calle

Assistant Professor, member of IXA
and HiTZ

Person 2

Ander Barrena

Assistant Professor at IXA
and HiTZ

Practical details

General information

Bring your own laptop (in order to do the practical side).
Part of the Language Analysis and Processing master program.
5 theoretical sessions with interleaved hands-on labs (20 hours).
Scheduled from July 4th to 8th 2022, 15:00-19:00 CET.

Where: "Ada Lovelace", Computer science faculty, San Sebastian
(practical classes will be held in labs, split groups).
The university provides some limited information about accommodation in San Sebastian (Basque/Spanish) and the Basque Country (English).
Teaching language: English.
Capacity: 60 attendants (First-come first-served).
Cost: 270€ + 4€ insurance = 274€
(270€ if you are an UPV/EHU member or have already registered for another course).


Registration open: now to the 26th of June 2022 (or until room is full).
Please register by email to (subject "Registration to DL4NLP" and CC
Same for any enquiry you might have.
The university provides official certificates for an additional fee. Please apply AFTER completing the course.
The university can provide invoices addressed to universities or companies. More details are provided after registration is made.

Basic programming experience, a university-level course in computer science and experience in Python. Basic math skills (algebra or pre-calculus) are also needed.
Bring your own laptop (no need to install anything).

Previous editions

Online class of July 2020 (left), with a handful of the 70 participants. To the right the screen lecturers had to talk to :-)

Class of January 2020

Class of July 2019