SubjectsSubjects(version: 902)
Course, academic year 2021/2022
  
Neural Networks - AM445004
Title: Neural Networks
Guaranteed by: Department of Computing and Control Engineering (445)
Actual: from 2019 to 2021
Semester: summer
Points: summer s.:5
E-Credits: summer s.:5
Examination process: summer s.:
Hours per week, examination: summer s.:2/2 [hours/week]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
Language: English
Teaching methods: full-time
Level:  
For type: Master's (post-Bachelor)
Guarantor: Mudrová Martina Ing. Ph.D.
Procházka Aleš prof. Ing. CSc.
Incompatibility : M445004
Interchangeability : M445004
Is incompatible with: M445004
Is interchangeable with: M445004
Annotation
Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (14.06.2022)
The course is focused on comprehension of commonly used neural network architectures, suitable for various types of solved problems and processed data. Lectures cover the necessary theory, but are mainly focused on practical aspects of neural network design. For seminars, students will try to train the designed models of neural networks and further optimize them.
Literature
Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (14.06.2022)

Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org

Syllabus
Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (14.06.2022)

Feed-forward neural networks

  • basic architectures and activation functions
  • optimization algorithms for training
  • selection of hyperparameters

Regularization of neural network models

  • commonly used regularization techniques - dropout, label-smoothing

Convolutional neural networks

  • convolution layers, normalization
  • architectures suitable for deep convolutional neural networks
  • pre-training and fine-tuning of deep neural networks

Recurrent neural networks

  • basic recurrent networks and problems of their training
  • LSTM, GRU
  • bidirectional and deep recurrent networks

Transformer architecture

Design and optimization of neural networks in various environments - Python, MATLAB

 
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