SubjectsSubjects(version: 955)
Course, academic year 2023/2024
Neural Networks - M445004
Title: Neuronové sítě
Guaranteed by: Department of Mathematics, Informatics and Cybernetics (446)
Faculty: Faculty of Chemical Engineering
Actual: from 2023
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
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Additional information:
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Cejnar Pavel RNDr. Mgr. Ph.D.
Incompatibility : AM445004
Interchangeability : AM445004, N445024
Is incompatible with: AM445004
Is interchangeable with: AM445004
This subject contains the following additional online materials
Annotation -
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.
Last update: Cejnar Pavel (14.06.2022)
Aim of the course -

Students will be able to:

(i) select the appropriate neural network architecture for the selected data type

(ii) design the neural network model and select the appropriate optimization algorithm for training

Last update: Cejnar Pavel (14.06.2022)
Literature -

A: Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016.

Last update: Cejnar Pavel (22.09.2023)
Learning resources -

Last update: Cejnar Pavel (14.06.2022)
Syllabus -

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.

Last update: Cejnar Pavel (22.09.2023)
Entry requirements -

basic programming skills in Python, MATLAB (at least one of them) are advisable

Last update: Cejnar Pavel (14.06.2022)
Course completion requirements -

The student passes the practicals by submission of sufficient number of assignments (obtaining the appropriate number of points, including bonus points). The assignments are announced regularly during the whole semester. The student can choose which of the assignments to work on in order to obtain the necessary number of points. The written exam test consists of randomly selected questions from a set of previously announced exam questions. Classification in the exam can be improved or replaced by submission of an extended number of assignments (obtaining the extended number of points).

Last update: Cejnar Pavel (14.06.2022)
Teaching methods
Activity Credits Hours
Účast na přednáškách 1 28
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 1 28
Práce na individuálním projektu 1 28
Příprava na zkoušku a její absolvování 1 28
Účast na seminářích 1 28
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