Neural Networks 2

The course is not on the list Without time-table
Code Completion Credits Range Language
18NES2 KZ 3 0P+2C Czech
Garant předmětu:
Zuzana Petříčková
Zuzana Petříčková, František Voldřich
Zuzana Petříčková, František Voldřich
Department of Software Engineering

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.

Syllabus of lectures:

1. Introduction to Deep Learning: History and Basic Concepts. Existing Frameworks for Deep Learning

2. Deep Neural Networks: Architectures and Activation Functions

3. Algorithms for Deep Neural Network Training

4. Hyperparameter Optimization and Tuning

5. Regularization Techniques for Deep Neural Networks

6. Convolutional Neural Networks: Basics and Principles

7. Architectures of Convolutional Neural Networks.

8. Convolutional Network Architectures for Object Detection and Segmentation

9. Pre-training and Fine-tuning of Deep Neural Networks. Transfer learning.

10. Recurrent Neural Networks and Sequential Data Processing

11. Architectures of Recurrent Neural Networks

12. Autoencoders: Principles and Applications

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

Syllabus of tutorials:

The structure of the exercises corresponds to the structure of the lectures. The exercises will focus on experimenting with various deep learning models using popular frameworks (such as 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.

Study Objective:

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

Study materials:

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

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

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

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

Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-06-16
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