Neural Networks 2

Course guarantor:

Lecturer:

Zuzana Petříčková

Tutor:

Zuzana Petříčková

Supervisor:

Department of Software Engineering

Synopsis:

The aim of the course „Neural Networks 2“ is to acquaint students with basic models of deep neural networks and teach them how to apply these models and methods to solve practical tasks.

Requirements:

Course Requirements

1) Mandatory: Completion of a project and its in-person presentation during the lecture (on December 16, 2025, or earlier).

2) Voluntary (but contributes to the final grade): Participation in practical sessions. Independent preparation and individual consultations may be permitted upon prior agreement with the instructor.

Mandatory Deadlines

1) The student shall select a project topic and obtain the instructors approval no later than 18 November 2025.

2) The student shall submit and present the project in front of other students during an exercise session on a date arranged in advance, but no later than the week commencing 15 December 2025.

3) Should the project not be completed on time, the student shall present its current state during the exercise session. The final version must subsequently be submitted and defended during an individual consultation no later than 4 September 2026.

Details: http://zuzka.petricek.net/vyuka_2025/NES2_2025/credits.php

Syllabus of lectures:

Syllabus of tutorials:

The exercises will focus on experimenting with various deep learning models using popular frameworks (such as Keras, TensorFlow or PyTorch) on practical tasks (processing image and sequential data, object detection, segmentation, etc.). Students will gain experience in analyzing results and learn about practical aspects of model implementation and tuning, which will help them better understand deep learning.

1. Introduction to Deep Learning: history, basic concepts. Frameworks (Keras, TensorFlow, PyTorch) and their usage

2. Fundamentals of Deep Neural Networks: architectures, activation functions, implementation and training on a sample dataset

3. Basic task types (classification, regression, time series prediction) specifics and examples

4. Data for Deep Learning: acquisition, preprocessing, exploratory analysis, normalization, standardization, augmentation

56. Image classification: convolutional neural networks (CNN), principles, implementation, selected architectures

7. Training and tuning models: optimization, hyperparameter tuning, regularization, learning strategies, pretrained models and transfer learning

8. Advanced CNN applications: object detection, segmentation, encoderdecoder architectures

9. Modeling sequential data: time series, recurrent neural networks (RNN, LSTM, GRU)

10. Natural language processing: from RNNs to Transformers, practical examples (e.g., sentiment analysis)

1112. Generative models: autoencoders, variational autoencoders, GANs and their applications

12-13. Student project presentations

Study Objective:

Students will become familiar with various basic models of deep neural networks (including feedforward networks, convolutional neural networks, recurrent neural networks, and chosen generative models). They will learn how to implement and apply the discussed models and methods to solve practical tasks.

Study materials:

[1] F. Chollet, M. Watson: Deep Learning with Python, Second Edition, 2021 (Third Edition - 2025).

[2] M. Nielson: Neural Networks and Deep Learning, 2019.

[3] A. Kapoor , A. Gulli , S. Pal: Deep Learning with TensorFlow and Keras 3rd edition, 2022.

[4] Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning, 2016, MIT Press

[5] Charu C. Aggarwal: Neural Networks and Deep Learning: A Textbook, 2018, Springer

[6] Ivan Vasilev, Daniel Slater: Python Deep Learning, 2019, Packt Publishing

[7] Andrew W. Trask: Grokking Deep Learning, 2019, Manning Publications

Note:

Further information:

http://zuzka.petricek.net/

Time-table for winter semester 2025/2026:

Time-table is not available yet

Time-table for summer semester 2025/2026:

Time-table is not available yet

The course is a part of the following study plans:

• Aplikace informatiky v přírodních vědách (elective course)
• Aplikované matematicko-stochastické metody (elective course)
• Kvantové technologie (elective course)