Backpropagation (machine learning)
Last revised by Frank Gaillard on 4 Oct 2020
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Adams M, Gaillard F, Gajera J, et al. Backpropagation (machine learning). Reference article, Radiopaedia.org (Accessed on 20 May 2024) https://doi.org/10.53347/rID-56164
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56164
Article created:
15 Oct 2017,
Matt Adams ◉
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At the time the article was created Matt Adams had no recorded disclosures.
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Last revised:
Disclosures:
At the time the article was last revised Frank Gaillard had no recorded disclosures.
View Frank Gaillard's current disclosures
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Backpropagation in supervised machine learning is the process used to calculate the gradient of the error function associated with each parameter weighting within a convoluted neural network (CNN). Essentially, the gradient estimates how the system parameters should change in order to optimize the network overall 1,2.
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References
- 1. Nikhil Buduma, Nicholas Locascio. Fundamentals of Deep Learning. ISBN: 9781491925614
- 2. Stephen Marsland. Machine Learning. ISBN: 9781498759786
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