An Introductory Review of Deep Learning for Prediction Models With Big Data
Tampere University · University of Applied Sciences Upper Austria · +2 more institutions
Abstract
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem possible. On a downside, the mathematical and computational methodology underlying deep learning models is very challenging, especially for interdisciplinary scientists. For this reason, we present in this paper an introductory review of deep learning approaches including Deep Feedforward Neural Networks (D-FFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AEs), and Long…
Citation impact
- FWCI
- 54.26
- Percentile
- 100%
- References
- 170
Authors
5- FEFrank Emmert‐StreibCorresponding
Tampere University
- ZYZhen Yang
Tampere University
- FHFeng Han
University of Applied Sciences Upper Austria, Tampere University
- STShailesh Tripathi
University of Applied Sciences Upper Austria, Tampere University
- MDMatthias Dehmer
University of Applied Sciences Upper Austria, UMIT - Private Universität für Gesundheitswissenschaften, Medizinische Informatik und Technik, Nankai University
Topics & keywords
- Deep learning
- Artificial intelligence
- Computer science
- Toolbox
- Field (mathematics)
- Convolutional neural network
- Machine learning
- Artificial neural network