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:

Conditions for Course Credit:

1) Project

The student completes a project on a chosen topic, which must be approved by the instructor no later than November 25, 2025.

The project must be submitted and presented during the lab session on a pre-agreed date.

Presentation in front of other students is mandatory, taking place no later than the week of December 15, 2025.

If the project is not completed on time, the student presents the current progress during the lab. The final version must then be presented during an individual consultation no later than September 4, 2026.

2) Attendance in labs

The student regularly attends lab sessions.

In justified cases (e.g., timetable conflicts), this requirement may be replaced by independent study/preparation, subject to prior agreement with the instructor.

Evaluation

The final grade is based on:

1) the quality of the completed project and compliance with deadlines,

2) active participation in lab sessions (regular attendance, following the lectures, engaging in tasks, experimenting, asking questions).

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 and Basic Concepts. Existing Frameworks for Deep Learning. Basic Work with Keras, TensorFlow or PyTorch. Creating a Simple Neural Network with Numerical Data.

2. Deep Neural Networks: Architectures and Activation Functions. Implementing and Training a Deep Neural Network on the MNIST Dataset.

3. Introduction to Solving Basic Types of Tasks (Classification, Regression, Time Series Prediction). Specifics of Each Type of Task.

4. Convolutional Neural Networks: Basics and Principles. Classification Tasks. Architectures of Convolutional Neural Networks.

5. Deep Learning and Data. Acquisition, Preparation, and Processing of Data. Normalization and Standardization. Data Augmentation.

6. Algorithms for Deep Neural Network Training, Hyperparameter Optimization and Tuning (Grid Search, Random Search, Bayesian Optimization), Regularization Techniques for Deep Neural Networks.

7. Pre-training and Fine-tuning of Deep Neural Networks. Transfer Learning.

8. Recurrent Neural Networks and Sequential Data Processing.

9. Architectures of Recurrent Neural Networks.

10-11. Convolutional Network Architectures for Object Detection and Segmentation.

12. Autoencoders: Principles and Applications (Denoising, Dimensionality Reduction).

13. Introduction to Other Neural Network Models (Generative Models, Transformers, Reinforcement Learning).

Study Objective:

Students will become familiar with various basic models of deep neural networks (including feedforward networks, convolutional neural networks, recurrent neural networks, and encoders). 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:

• Quantum Technologies (elective course)