Deep learning (DL) is a family of techniques widely used in multiple fields with excellent results. Unfortunately, DL has a steep learning curve. Fortunately, several domain-specific libraries have been developed to facilitate the use of DL models.
For the widespread adoption of these techniques, researchers should be able to design and use their own DL models. Image classification is one of the main applications of DL in astrophysics and offers a convenient way to learn about neural networks. The de facto standard to tackle this problem is Convolutional Neural Networks (CNN), a concrete deep learning architecture. This course will serve as an introduction to Deep Learning with four sessions with the following objectives: understanding the basics of neural networks, getting to know the fundamental libraries and fundamental architectures, learning how to train a CNN, gaining confidence in using CNNs for image classification tasks, and learning how to evaluate their preformance.
The tutor of this school is Dr Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla).
There will be coffee breaks available to participants.
For more details, see the agenda below.
The repository with contents and materials can be found in Github (TBC)
This course will be an in-person event, and it is limited to 25 participants in order to assure adequate interaction and individualised attention. Inscription will be handled on a first come, first serve basis. To formalize the participation you must fill the mandatory fields in the registration form https://form.jotform.com/250704428022346.
The registration fee is 135 euros. It includes two coffee breaks daily.
This event is supported by the "Center of Excellence Severo Ochoa" award to the Instituto de Astrofísica de Andalucía. We acknowledge financial support from the Severo Ochoa grant CEX2021-001131-S funded by MCIN/AEI/ 10.13039/501100011033 from the Instituto de Astrofísica de Andalucía.