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Deep Learning

Deep Learning
460-4140/01
čeština Deep Learning
460-4140/02
angličtina

Summary

In the course, students will get acquainted with deep learning methods with particular emphasis on deep neural networks. Students build on their knowledge of machine learning and deepen it through demonstrations and a deep learning approach to various data types, from vectors, images, text, or data streams. Students will have the chance to test their knowledge and skills using appropriate tools and libraries over artificial and real data and interpret the results for their complete understanding.

Literature

- Slides from Lectures
[1] GOODFELLOW, Ian, Yoshua BENGIO a Aaron COURVILLE. Deep learning. Illustrated edition. Cambridge, MA: MIT press, 2016. Adaptive computation and machine learning series. ISBN 978-0262035613.
[2] SAITOH, Koki. Deep Learning from the Basics: Python and Deep Learning: Theory and Implementation. Birmingham, UK: Packt Publishing, 2021. ISBN 978-1800206137 .
[3] GÉRON, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. Second edition. Beijing: O'Reilly, 2019. ISBN 978-1-4920-3264-9.
[4] HOWARD, Jeremy a Sylvain GUGGER. Deep learning for coders with Fastai and PyTorch: Ai applications without a PhD. Sebastopol, CA: O´Reilly, 2020. ISBN 978-1-492-04552-6.
[5] KELLEHER, John D. Deep learning. Illustrated edition. Cambridge: The MIT Press, 2019. MIT Press essential knowledge series. ISBN 978-0262537551 .
[6] KROHN, Jon, Grant BEYLEVELD a Aglaé BASSENS. Deep learning illustrated: a visual, interactive guide to artificial intelligence. Boston: Addison-Wesley, [2020]. ISBN 978-0135116692 .

Advised literature

[1] AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1 .
[2] BRAMER, M. A. Principles of data mining. London: Springer, 2007. ISBN 1-84628-765-0 .
[3] LESKOVEC, Jure, Anand RAJARAMAN a Jeffrey D. ULLMAN. Mining of massive datasets, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232.
[4] WITTEN, Ian H., Eibe FRANK, Mark A. HALL a Christopher J. PAL. Data mining: Practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, [2017]. ISBN 978-0-12-804291-5.
[5] ZAKI, Mohammed J. a Wagner MEIRA JR. Data Mining and Analysis: Fundamental Concepts and Algorithms. 2nd edition. Cambridge, GB: Cambridge University Press, 2020. ISBN 978-0521766333.
[6] LAPAN, Maxim. Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. 1. Birmingham, UK: Packt Publishing, 2018. ISBN 978-1788839303 .


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Language of instruction čeština, angličtina
Code 460-4140
Abbreviation HU
Course title Deep Learning
Coordinating department Department of Computer Science
Course coordinator prof. Ing. Jan Platoš, Ph.D.