Bibliography

1

Kevin P. Murphy. Probabilistic Machine Learning: An introduction. MIT Press, 2022. URL: probml.ai.

2

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

3

Simon J.D. Prince. Understanding Deep Learning. MIT Press, 2023. URL: http://udlbook.com.

4

Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Dive into Deep Learning. Cambridge University Press, 2023. https://D2L.ai.

5

T. Hastie, R. Tibshirani, and J.H. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer series in statistics. Springer, 2009. ISBN 9780387848846. URL: https://books.google.kz/books?id=eBSgoAEACAAJ.

6

H. Zou and T. Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301–320, 2003.

7

F. Rosenblatt. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6):386–408, 1958. URL: http://dx.doi.org/10.1037/h0042519, doi:10.1037/h0042519.

8

Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In International Conference on Artificial Intelligence and Statistics. 2010. URL: https://api.semanticscholar.org/CorpusID:5575601.

9

Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. 2015 IEEE International Conference on Computer Vision (ICCV), pages 1026–1034, 2015. URL: https://api.semanticscholar.org/CorpusID:13740328.

10

Christopher M Bishop. Pattern Recognition and Machine Learning. Volume 4 of Information science and statistics. Springer, 2006. ISBN 9780387310732. URL: http://www.library.wisc.edu/selectedtocs/bg0137.pdf, arXiv:0-387-31073-8, doi:10.1117/1.2819119.