About Deep Learning
Deep Learning
About this Collection
We have come a long way since the concept of Artificial Intelligence was first discussed in 1950’s, and now enjoy many everyday products and services that rely on it such as digital assistants, personalised recommendations, credit card fraud detection, and improved healthcare treatments.
Deep Learning is a subfield of Machine Learning which focuses on teaching computers to behave like a human brain to process data, create patterns, and make decisions. It does so by employing multiple layers, called neural networks, able to ‘learn’ from large amounts of data. The more a deep learning algorithm performs a task, the better it becomes at it, in the same way we humans learn from past experiences.
The aim of this collection is to provide Horizon 2020 and Horizon Europe beneficiaries with a space to openly publish work within Deep Learning and its applications, in line with their grant open access policy.
We offer a wide range of research outputs including research articles, brief reports, data notes, method articles, software tool articles, registered reports, reviews, and open letters, among others.
Potential topics include but are not limited to:
Open Research Europe requires open access to research data supporting articles under the principle ‘as open as possible, as closed as necessary’. All articles should include citations to repositories that host the data underlying the results, together with any information needed to replicate, validate, and/or reuse the results/your study and analysis of the data. We recognise there may be exceptions due to ethical, data protection, or confidentiality considerations, or because the data have been obtained from a third party and access restrictions apply.
Deep Learning is a subfield of Machine Learning which focuses on teaching computers to behave like a human brain to process data, create patterns, and make decisions. It does so by employing multiple layers, called neural networks, able to ‘learn’ from large amounts of data. The more a deep learning algorithm performs a task, the better it becomes at it, in the same way we humans learn from past experiences.
The aim of this collection is to provide Horizon 2020 and Horizon Europe beneficiaries with a space to openly publish work within Deep Learning and its applications, in line with their grant open access policy.
We offer a wide range of research outputs including research articles, brief reports, data notes, method articles, software tool articles, registered reports, reviews, and open letters, among others.
Potential topics include but are not limited to:
- Deep neural and belief networks
- Deep reinforcement learning
- Recurrent and convolutional neural networks
- Machine Translation
- Natural Language Processing
- Unsupervised, semi and supervised learning
Open Research Europe requires open access to research data supporting articles under the principle ‘as open as possible, as closed as necessary’. All articles should include citations to repositories that host the data underlying the results, together with any information needed to replicate, validate, and/or reuse the results/your study and analysis of the data. We recognise there may be exceptions due to ethical, data protection, or confidentiality considerations, or because the data have been obtained from a third party and access restrictions apply.