Deep learning (DL) is a family of techniques widely used in multiple fields with excellent results. Unfortunately, due to its steep learning curve, its use is not as extended as desirable. Several domain-specific libraries have been developed to facilitate the use of these DL models with modest success. 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 problems in astrophysics. The de facto standard to tackle this problem is Convolutional Neural Networks (CNN), a concrete deep learning architecture. A good knowledge of this technique is essential for its proper application by researchers. These three sessions have been designed with a goal in mind: to gain confidence in using CNNs for image classification tasks.
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 https://github.com/iaa-so-training/basic-neural-network-2023
This course will be an in-person event, and it is limited to 25 pariticipants 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. Payment can be made by bank transfer.
The registration is currently closed because we have reached full capacity for the event. To be added to the waiting list, contact Laura Darriba.
The registration fee is 50 euros. It includes two coffee breaks on Thursday and one on Friday.
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.