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Deep Learning

Type of study Follow-up Master
Language of instruction Czech
Code 460-4140/01
Abbreviation HU
Course title Deep Learning
Credits 4
Coordinating department Department of Computer Science
Course coordinator prof. Ing. Jan Platoš, Ph.D.

Subject syllabus

Lectures (topics):
1. Neural networks, principles, basic properties.
2. Neural networks - parameters.
3. Convolutional neural networks.
4. Autocoder.
5. Variation car encoder.
6. Recurrent neural networks.
7. Analysis of time series using neural networks.
8. Text classification - word representation
9. Language modeling using RNN
10. Vector data processing - Exploratory analysis and classification
11. Locating and recognizing objects in the image
12. Generative methods - GAN

Exercises in the computer room:
1. Neural networks, principles, basic properties.
2. Neural networks - parameters.
3. Convolutional neural networks.
4. Autocoder.
5. Variation car encoder.
6. Recurrent neural networks.
7. Analysis of time series using neural networks.
8. Text classification - word representation
9. Language modeling using RNN
10. Vector data processing - Exploratory analysis and classification
11. Locating and recognizing objects in the image
12. Generative methods - GAN

E-learning

Selected study materials are published on the e-learning portal (https://www.vsb.cz/e-
vyuka/en).

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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