SubjectsSubjects(version: 955)
Course, academic year 2019/2020
Neural Networks - M445004
Title: Neuronové sítě
Guaranteed by: Department of Computing and Control Engineering (445)
Faculty: Faculty of Chemical Engineering
Actual: from 2019 to 2019
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: unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Teaching methods: full-time
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Procházka Aleš prof. Ing. CSc.
Mudrová Martina Ing. Ph.D.
Incompatibility : AM445004
Interchangeability : AM445004, N445024
Is incompatible with: AM445004
Is interchangeable with: AM445004
Examination dates   Schedule   
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 - Czech

Studenti budou umět:

(i) vybrat vhodnou architekturu neuronové sítě pro zvolený typ dat

(ii) navrhnout příslušný model a vybrat vhodný optimalizační algoritmus pro trénování

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 - Czech

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)
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
5 / 5 140 / 140
Coursework assessment
Form Significance
Regular attendance 30
Report from individual projects 30
Oral examination 40