Artificial Neural Networks: from the Ground Up
Since their earliest conception in the 1940s, artificial neural networks have been alternatively regarded as extremely promising machine learning models, capable of learning anything, and as glorified linear combinations, unable to achieve relevant results in practice.
However, along the last decade, the availability of general-purpose GPU architectures and large quantities of data has enabled the rise of deep neural networks, which have attained state-of-the-art performance in many applications, from image classification to text translation. This has given rise to a whole new field of research, ranging from generative models to adversarial attacks (and defenses against them).
Practical information:
Want to register?
- Prerequisites: Basic knowledge of the Python programming language is required (as for instance taught in Module 4 of this year's program).
- Price: Industry, private sector, profession*-925€---Nonprofit, government, higher education staff-695€---Temporary Discount for UGent Alumni and UGent staff-462.5€---(Doctoral) student, unemployed-310€---Exam Fee 35€---*If two or more employees from the same company enroll simultaneously for this course a reduction of 20% on the module price is taken into account, starting from the second enrolment.
Schedule
Five Thursday evenings in April and May 2023: April 20 and 27, May 4, 11 and 25, 2023, from 5.30 pm to 9 pm
Georganiseerd door:
General Information
This course is intended as a first contact with artificial neural networks, followed by an overview of the different architectures that are currently available:
- Introduction to neurons and neural networks
- Training with backpropagation
- Challenges and solutions to train deep neural networks
- Convolutional networks
- Adversarial examples
- Generative models
- Autoregressive models
- Autoencoders
- Variational autoencoders (VAE)
- Generative adversarial networks (GAN)
- Transformers and BERT
- Recurrent neural networks
The practical sessions use the Python library TensorFlow to implement some of the models discussed in the course, with particular emphasis on how to adapt the networks to the characteristics of a specific problem.
Teacher / speaker
Daniel Peralta
Dr. Daniel Peralta is a post-doctoral researcher at the Department of Applied Mathematics, Computer Science and Statistics of the Faculty of Sciences of Ghent University. He obtained his PhD at the University of Granada (Spain), tackling large-scale fingerprint identification.
His research has focused on machine learning, especially in large-scale scenarios, and has involved several collaborations with industry to apply such techniques on problems ranging from railway maintenance scheduling to compound activity prediction. Within his current position at the VIB, this research is applied on biological data. He currently teaches Big Data Science courses at Ghent University, in the Master of Statistical Data Analysis and the Master in Computer Science.
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