| DC Field | Value | Language |
|---|
| dc.contributor.author | DERMACHE, MOhamed Djamel | - |
| dc.contributor.author | BOULARAOUI, MOhamed El Amine | - |
| dc.date.accessioned | 2022-11-10T08:01:27Z | - |
| dc.date.available | 2022-11-10T08:01:27Z | - |
| dc.date.issued | 2022 | - |
| dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/344 | - |
| dc.description | Encadreur : Dr ATTAOUI Mohammed Oualid Co-Encadreur : Pr BENSLIMANE Sidi Mohammed | en_US |
| dc.description.abstract | Transfer learning is the act of transferring pre-existing knowledge to another model to
solve similar problem, due to the recent world pandamic caused by the newly discovered
COVID-19 virus, the concept of transfer learning can make a major impact in finding a
proper solution in a faster time which is crucial in this case.
Transfer learning can be applied in many ways including using it with fine tuning. Each
way needs its proper preparations and considerations. The goal of this paper is to explore
the subject in detail and go through an extensive comparison on the most widely ways and
models using these techniques in methods like classification and clustering. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Transfer Learning | en_US |
| dc.subject | Fine Tuning | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Classification | en_US |
| dc.subject | Clustering | en_US |
| dc.title | Predicting COVID-19 from chest X-ray images using fine tuning and transfer learning | en_US |
| dc.type | Thesis | en_US |
| Appears in Collections: | Ingénieur
|
