460-4140/01 – Deep Learning (HU)
Gurantor department | Department of Computer Science | Credits | 4 |
Subject guarantor | prof. Ing. Jan Platoš, Ph.D. | Subject version guarantor | prof. Ing. Jan Platoš, Ph.D. |
Study level | undergraduate or graduate | Requirement | Optional |
Year | 1 | Semester | summer |
| | Study language | Czech |
Year of introduction | 2022/2023 | Year of cancellation | |
Intended for the faculties | FEI, FMT | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The course aims to acquaint students with deep learning methods, deep neural networks, and other methods of advanced data processing. Students will be acquainted with basic and advanced deep learning methods and their applications over vector, image, text, and other data.
Teaching methods
Lectures
Tutorials
Summary
In the course, students will get acquainted with deep learning methods with particular emphasis on deep neural networks. Students build on their knowledge of machine learning and deepen it through demonstrations and a deep learning approach to various data types, from vectors, images, text, or data streams. Students will have the chance to test their knowledge and skills using appropriate tools and libraries over artificial and real data and interpret the results for their complete understanding.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
The student knowledge is checked during lab using exercises, home works and implementation of selected algorithm.
E-learning
Other requirements
Additional requirements are not placed on the student.
Prerequisities
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures (topics):
1. Neural networks, principles, basic properties.
2. Neural networks - parameters.
3. Convolutional neural networks.
4. Autocoder.
5. Variation car encoder.
6. Recurrent neural networks.
7. Analysis of time series using neural networks.
8. Text classification - word representation
9. Language modeling using RNN
10. Vector data processing - Exploratory analysis and classification
11. Locating and recognizing objects in the image
12. Generative methods - GAN
Exercises in the computer room:
1. Neural networks, principles, basic properties.
2. Neural networks - parameters.
3. Convolutional neural networks.
4. Autocoder.
5. Variation car encoder.
6. Recurrent neural networks.
7. Analysis of time series using neural networks.
8. Text classification - word representation
9. Language modeling using RNN
10. Vector data processing - Exploratory analysis and classification
11. Locating and recognizing objects in the image
12. Generative methods - GAN
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction