Description
Description
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).
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.
Fees and registration
Fees and registration form are available on the website of the Academy for Lifelong Learning of the Faculty of Sciences (UGent).
Target audience
This course is aimed at professionals and investigators from diverse areas who want to learn how to apply neural networks on diverse problems, or who want to learn about the possibilities, applicability, and variants of neural networks.
Course prerequisites
Basic knowledge of the Python programming language is required (as for instance taught in Module 4 of this year's program).
Exam / Certificate
If you take part in all 5 sessions you will receive a certificate of attendance via e-mail after the course ends.
Additionally, you can take part in an exam. If you succeed in this test a certificate from Ghent University is issued.
The exam consists of a take home project assignment. You are required to write a report by a set deadline.
Type of course
This is an on campus course. We offer blended learning options if, exceptionally, you can't attend a class on campus.
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
Venue
Faculty of Science, Campus Sterre, Krijgslaan 281, Building S9, Ghent
Course material
Acces to slides and code for the practical sessions
