The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Fundamentals of Artificial Neural Networks and Deep Learning
Open accessAbstract
In this chapter, we go through the fundamentals of artificial neural networks and deep learning methods. We describe the inspiration for artificial neural networks and how the methods of deep learning are built. We define the activation function and its role in capturing nonlinear patterns in the input data. We explain the universal approximation theorem for understanding the power and limitation of these methods and describe the main topologies of artificial neural networks that play an important role in the successful implementation of these methods. We also describe loss functions (and their penalized versions) and give details about in which circumstances each of them should be used or preferred. In addition to the Ridge, Lasso, and Elastic Net regularization methods, we provide details of the dropout and the early stopping methods. Finally, we provide the backpropagation method and illustrate it with two simple artificial neural networks.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106
Neural Networks And Learning Machines
Haykin, S. (2010) · Cited by 6,609
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873
Electricity price forecasting: A review of the state-of-the-art with a look into the future
Weron, R. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.08.008 · Cited by 1,646
Metadata
- Authors
- Montesinos López, O. A., Montesinos López, A., & Crossa, J.
- Venue
- Multivariate Statistical Machine Learning Methods for Genomic Prediction
- Publisher
- Springer International Publishing
- Published
- 2022
- Pages
- 379-425
- OpenAlex
- W4205995027
- Cited by
- 283
- References
- 26
- FWCI
- 117.68
- Type
- book chapter
- Language
- en
- Open access
- hybrid
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