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Last update: RNDr. Jiří Mírovský, Ph.D. (16.03.2024)
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Last update: RNDr. Jiří Mírovský, Ph.D. (11.05.2023)
The goal of the course is to introduce deep neural networks, from the basics to the latest advances. The course will focus both on theory as well as on practical aspects. |
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Last update: RNDr. Jiří Mírovský, Ph.D. (11.05.2023)
Students pass the practicals by submitting sufficient number of assignments. The assignments are announced regularly the whole semester and are due in several weeks. Considering the rules for completing the practicals, it is not possible to retry passing it. Passing the practicals is not a requirement for going to the exam. |
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Last update: RNDr. Jiří Mírovský, Ph.D. (11.05.2023)
Yoshua Bengio, Ian Goodfellow, Aaron Courville: Deep learning, MIT Press, In preparation. Jürgen Schmidhuber: Deep learning in neural networks: An overview, Neural networks 61 (2015): 85-117. Sepp Hochreiter, and Jürgen Schmidhuber: Long short-term memory, Neural computation 9.8 (1997): 1735-1780. |
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Last update: RNDr. Jiří Mírovský, Ph.D. (11.05.2023)
The exam is written and consists of questions randomly chosen from a publicly known list. The requirements of the exam correspond to the course syllabus, in the level of detail which was presented on the lectures. |
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Last update: RNDr. Milan Straka, Ph.D. (07.02.2024)
Feedforward deep neural networks
Regularization of deep models
Convolutional neural networks
Recurrent neural networks
Practical methodology
Natural language processing
Deep generative models
Structured prediction
Introduction to deep reinforcement learning |
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Last update: RNDr. Milan Straka, Ph.D. (08.11.2023)
Basic programming skills in Python are required. No previous knowledge of artificial neural networks is needed, but basic understanding of machine learning is advantageous. |