Version 1
: Received: 11 August 2019 / Approved: 13 August 2019 / Online: 13 August 2019 (09:32:09 CEST)
How to cite:
Mosavi, A.; Faizollahzadeh ardabili, S.; R. Várkonyi-Kóczy, A. List of Deep Learning Models. Preprints2019, 2019080152. https://doi.org/10.20944/preprints201908.0152.v1
Mosavi, A.; Faizollahzadeh ardabili, S.; R. Várkonyi-Kóczy, A. List of Deep Learning Models. Preprints 2019, 2019080152. https://doi.org/10.20944/preprints201908.0152.v1
Mosavi, A.; Faizollahzadeh ardabili, S.; R. Várkonyi-Kóczy, A. List of Deep Learning Models. Preprints2019, 2019080152. https://doi.org/10.20944/preprints201908.0152.v1
APA Style
Mosavi, A., Faizollahzadeh ardabili, S., & R. Várkonyi-Kóczy, A. (2019). List of Deep Learning Models. Preprints. https://doi.org/10.20944/preprints201908.0152.v1
Chicago/Turabian Style
Mosavi, A., Sina Faizollahzadeh ardabili and Annamária R. Várkonyi-Kóczy. 2019 "List of Deep Learning Models" Preprints. https://doi.org/10.20944/preprints201908.0152.v1
Abstract
Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.
Keywords
deep learning; machine learning model; convolutional neural networks (CNN); recurrent neural networks (RNN); denoising autoencoder (DAE); deep belief networks (DBNs); long short-term memory (LSTM); review; survey; state of the art
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.