Темп навчання (Ukrainian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Темп навчання" in Ukrainian language version.

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1sciencedirect.com

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1probml.github.io

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1machinelearningmastery.com

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archive.org

Patterson, Josh; Gibson, Adam (2017). Understanding Learning Rates. Deep Learning : A Practitioner's Approach. O'Reilly. с. 258–263. ISBN 978-1-4919-1425-0. (англ.)
Dixon, L. C. W. (1972). The Choice of Step Length, a Crucial Factor in the Performance of Variable Metric Algorithms. Numerical Methods for Non-linear Optimization. London: Academic Press. с. 149–170. ISBN 0-12-455650-7. (англ.)

arxiv.org

Ruder, Sebastian (2017). An Overview of Gradient Descent Optimization Algorithms. arXiv:1609.04747.
An empirical study into finding optima in stochastic optimization of neural networks // Information Sciences. — 2021. — Т. 560 (11 лютого). — С. 235-255. — arXiv:1903.08552. (англ.)
Mutschler, Maximus; Zell, Andreas (2019). Parabolic Approximation Line Search for DNNs. arXiv:1903.11991. (англ.)
Mahsereci, Maren; Hennig, Phillip (2016). Probabilistic Line Searches for Stochastic Optimization. arXiv:1502.02846v4. (англ.)
Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches // Journal of Global Optimization. — 2021. — Т. 79 (11 лютого). — С. 111-152. — arXiv:2001.05113. (англ.)
Chae, Younghwan; Wilke, Daniel N. (2019). Empirical study towards understanding line search approximations for training neural networks. arXiv:1909.06893. (англ.)
Smith, Leslie N. (4 квітня 2017). Cyclical Learning Rates for Training Neural Networks. arXiv:1506.01186 [cs.CV]. (англ.)

google.com

Murphy, Kevin P. (2012). Machine Learning: A Probabilistic Perspective. Cambridge: MIT Press. с. 247. ISBN 978-0-262-01802-9. (англ.)
Nesterov, Y. (2004). Introductory Lectures on Convex Optimization: A Basic Course. Boston: Kluwer. с. 25. ISBN 1-4020-7553-7. (англ.)

machinelearningmastery.com

Brownlee, Jason (22 січня 2019). How to Configure the Learning Rate When Training Deep Learning Neural Networks. Machine Learning Mastery. Процитовано 4 січня 2021. (англ.)

probml.github.io

Murphy, Kevin (2021). Probabilistic Machine Learning: An Introduction. MIT Press. Процитовано 10 квітня 2021. (англ.)

psu.edu

citeseerx.ist.psu.edu

Delyon, Bernard (2000). Stochastic Approximation with Decreasing Gain: Convergence and Asymptotic Theory. Unpublished Lecture Notes. Université de Rennes. CiteSeerX 10.1.1.29.4428. (англ.)

sciencedirect.com

An empirical study into finding optima in stochastic optimization of neural networks // Information Sciences. — 2021. — Т. 560 (11 лютого). — С. 235-255. — arXiv:1903.08552. (англ.)

springer.com

link.springer.com

Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches // Journal of Global Optimization. — 2021. — Т. 79 (11 лютого). — С. 111-152. — arXiv:2001.05113. (англ.)