SubjectsSubjects(version: 949)
Course, academic year 2019/2020
  
Neural Networks - AM445004
Title: Neural Networks
Guaranteed by: Department of Computing and Control Engineering (445)
Faculty: Faculty of Chemical Engineering
Actual: from 2019 to 2020
Semester: summer
Points: summer s.:5
E-Credits: summer s.:5
Examination process: summer s.:
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
Language: English
Teaching methods: full-time
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
Examination dates   Schedule   
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. (22.09.2023)

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

Syllabus
Last update: Cejnar Pavel RNDr. Mgr. Ph.D. (22.09.2023)

1. Introduction to neural networks.

2. Feed-forward neural networks, basic architectures and activation functions.

3. Optimization algorithms for training.

4. Regularization of neural network models.

5. Frameworks for neural network development.

6. Convolutional neural networks, normalization.

7. Architectures suitable for deep convolutional neural networks.

8. Architectures for object detection and segmentation.

9. Pre-training and fine-tuning of deep neural networks.

10. Recurrent neural networks and problems of their training.

11. Recurrent neural networks - bidirectional and deep recurrent networks.

12. Transformer architecture.

13. Design and optimization of neural networks in various environments.

14. Reinforcement learning.

 
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