Subject

XSL Content

Deep Learning

General details of the subject

Mode
Face-to-face degree course
Language
English

Description and contextualization of the subject

Los modelos de redes neuronales de Aprendizaje Profundo (Deep Learning) se han aplicado con éxito al procesamiento del lenguaje natural, y están cambiando radicalmente la forma en que interactuamos con las máquinas (Siri, Amazon Alexa, Google Home, el traductor de Skype, Google Translate o el motor de búsqueda de Google). Estos modelos infieran una representación continua tanto para palabras como para oraciones, en lugar de utilizar los rasgos diseñadas a mano de otros enfoques de aprendizaje automático. El curso presentará los principales modelos de aprendizaje profundo utilizados en el procesamiento del lenguaje natural, lo que permitirá a los asistentes comprender e implementar estos modelos en Tensorflow.

Teaching staff

NameInstitutionCategoryDoctorTeaching profileAreaE-mail
AGIRRE BENGOA, ENEKOUniversity of the Basque CountryProfesorado PlenoDoctorBilingualComputer Languages and Systemse.agirre@ehu.eus
AZCUNE GALPARSORO, GORKAUniversity of the Basque CountryProfesorado Adjunto (Ayudante Doctor/A)DoctorBilingualScience of Computation and Artificial Intelligencegorka.azcune@ehu.eus
BARRENA MADINABEITIA, ANDERUniversity of the Basque CountryProfesorado Adjunto (Ayudante Doctor/A)DoctorBilingualComputer Languages and Systemsander.barrena@ehu.eus
LOPEZ DE LACALLE LECUONA, OIERUniversity of the Basque CountryProfesorado Adjunto (Ayudante Doctor/A)DoctorBilingualComputer Languages and Systemsoier.lopezdelacalle@ehu.eus

Competencies

NameWeight
Learn skills to deal with strategies and tools for natural language processing.20.0 %
Learn skills to deal with machine learning methods for natural language processing.20.0 %
Ability to manage, adapt and improve the most relevant empirical methods for research in language technologies.20.0 %
Ability to handle multimodal representations.20.0 %
Ability to improve language understanding with visual information.20.0 %

Study types

TypeFace-to-face hoursNon face-to-face hoursTotal hours
Lecture-based203050
Applied laboratory-based groups4060100

Training activities

NameHoursPercentage of classroom teaching
Lectures50.040 %
Prácticas con ordenador, laboratorio, salidas de campo, visitas externas100.040 %

Assessment systems

NameMinimum weightingMaximum weighting
Works and projects0.0 % 100.0 %

Learning outcomes of the subject

Show understanding of deep learning systems, as well as the main architectures used in NLP

Development of basic deep learning systems applied to NLP problems.

Show knowledge about the latest advances in deep learning for NLP

Temary

1. Introduction to machine learning and NLP with Tensorflow

2. Multilayer Perceptron

3. Word embeddings and recurrent neural networks

4. Seq2seq, neural machine translation and better RNNs

5. Attention, Neural machine Translation and Natural Language Inference

6. Transfer learning

7. Bridging the gap between natural languages and the visual world

8. Convolutional neural networks for text

Bibliography

Basic bibliography

Yoav Goldberg's Primer. http://u.cs.biu.ac.il/~yogo/nnlp.pdf Kyunghyun Cho¿'s course notes. http://arxiv.org/pdf/1511.07916.pdf The online version of the Goodfellow, Bengio, and Courville Deep Learning textbook. http://www.deeplearningbook.org/