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, instead of using hand-engineered features as in other machine learning approaches. 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 Tensorflow.

This course is a 35 hour introduction to the main deep learning models used in text processing. It combines theoretical and practical hands-on classes. Attendants will be able to understand and implement the models in Tensorflow.

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 Tensorflow

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

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

Embeddings and Recurrent Neural Networks

Representation learning
Word embeddings
From words to sequences: Recurrent Neural Networks (RNN)
LABORATORY: Sentiment analysis with RNNs

Seq2seq, Neural Machine Translation and better RNNs

Application of RNN:
. Language Models (sentence encoders)
. Language Generation (sentence decoders)
. Sequence to sequence models and Neural Machine Translation (I)
Problems with gradients in RNN
LABORATORY: Sentiment analysis with GRUs

Attention, Neural machine Translation and Natural Language Inference

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

Bridging the gap between natural languages and the visual world

Very brief introduction to Deep Learning for Computer Vision
Convolutional Neural Networks
Image captioning
Visual question answering
Text-based image generation
LABORATORY: Image captioning with CNNs and RNNs

Convolutional neural networks for text

CNNs for text
Deep learning frameworks
Last words
LABORATORY: Convolutional Neural Networks for Text


Person 1

Eneko Agirre

Professor, member of IXA

Person 2

Gorka Azkune

Asist. prof., member of IXA

Person 3

Olatz Perez de Vinaspre

Assist. prof., member of IXA

Person 2

Ander Barrena

Postdoc researcher at IXA

Practical details

General information

Bring your own laptop (in order to do the practical side).
Part of the Language Analysis and Processing master program.
14 theoretical sessions with interleaved labs (35 hours).
Scheduled from January 27th to February 13th 2020, 17:30-20:00.

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: 274 euros (270 for UPV/EHU members).


Registration open: now to the 20th of January 2020 (or until room is full).
Please register by email to (subject "Registration to DL4NLP" and CC
Same for any enquiry you might have.

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