2nd Workshop on
Deep Learning for Multimedia
Dublin, Ireland
Insight Dublin City University (21-22 May 2018)
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Instructors
Photos
Check the photos from 2018 on Facebook or Google Photos, as well as the ones from 2017.
Practical
Previous
Previous editions
- Deep Learning for Multimedia. Insight Dublin City University 2017.
- Deep Learning for Computer Vision UPC TelecomBCN. [2016] [2017] [2018]
- Deep Learning for Speech and Language UPC TelecomBCN. [2017] [2018]
- Introduction to Deep Learning. UPC TelecomBCN 2018.
- Deep Learning for Artificial Intelligence. UPC TelecomBCN 2017.
- Amaia Salvador and Santiago Pascual. “Hands on Keras and TensorFlow”. Persontyle 2017.
Related
Related courses
Deep Learning
- Alexander Amini & Ava Soleimany, “6.S191. Introduction to Deep Learning”. MIT, Winter 2018.
- François Fleuret, “EE 559: Deep Learning”. EPFL 2018.
- Roger Grosse, “CSC 321: Intro to Neural Networks and Machine Learning. University of Toronto 2018.
- Joan Bruna, “Stats212b: Topics on Deep Learning”. Berkeley University. Spring 2016.
- Joan Bruna, “Mathematic of Deep Learning”. NYU, Spring 2018.
- Yann LeCun, “Deep Learning: Nine Lectures at Collège de France”. Collège de France, Spring 2016. [Facebook page]
- Hugo Larochelle, “Neural Networks”. Université de Sheerbroke.
- Svetlana Lazebnik, “CS 598 LAZ: Cutting-Edge Trends in Deep Learning and Recognition”. University of Illinois at Urbana-Champaign. Spring 2017.
Computer Vision
- Fei-Fei Li, Andrej Karpathy, Justin Johnson, “CS231n: Convolutional Neural Networks for Visual Recognition”. Stanford University, Spring 2016.
- Sanja Fidler, “Deep Learning in Computer Vision”. University of Toronto, Winter 2016.
- Dhruv Batra, “ECE 6504: Deep learning for perception”. Virginia Tech, Fall 2015.
Speech and Language
- Richard Socher, Chris Manning, “CS224n: Natural Language Processing with Deep Learning”. Stanford University, Winter 2017.
- Phil Blunsom, “Deep Natural for Natural Language Processing”](https://github.com/oxford-cs-deepnlp-2017). Oxford University 2017.
- Thang Luong, Kyunghyun Cho, and Christopher Manning, “Neural Machine Translation - Tutorial” ACL 2016.
- Dhruv Batra, “ECE 6504: Deep learning for perception”. Virginia Tech, Fall 2015.
Sponsors
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Insight Centre for Data Analytics
Dublin City University
ETSETB Telecom BCN
Universitat Politècnica de Catalunya