Deep neural networks

elective monographs

Proficiency in Python programming.

The goal of the course is to show usage cases for deep neural networks. During the course state-of-the-art techniques, algorithms and tools will be presented. Among others two main blocks of the course will concern image classification and text processing.

1. Introduction to neural networks: activation functions, loss function, optimizers, regularization.

2. Hardware and software for deep learning.

3. Convolutional neural networks: classification, detection, segmentation.

4. Recurrent neural networks, Transformers.

5. Generative Adversarial Networks.

6. Reinforcement learning.

7. New advancements in the field of neural networks.

8. Applications (e.g., Alpha Go, ChatGPT).

Online books:

http://neuralnetworksanddeeplearning.com/

http://www.deeplearningbook.org/

Knowledge: the student

* has based in theory and well organized knowledge in the scope of machine learning, and in particular of problems related to neural networks learning algorithms as well as convolutional and recursive architecture [K_W08].

Abilities: the student is able to

* use English at the proficiency level B2+ of Common European Framework of Reference for Languages, with particular emphasis on the computer science terminology [K_U02];

* make use of a chosen modern library of machine learning procedures [K_U12];

* implement image classification algorithms using convolutional neural networks and text transformation algorithms using recursive neural networks [K_U13].

Social competences: the student is ready to

* critically evaluate acquired knowledge and information [K_K01];

* recognize the significance of knowledge in solving cognitive and practical problems and the importance of consulting experts when difficulties arise in finding a self-devised solution [K_K02];

* think and act in an entrepreneurial way [K_K03].

Final grade is based upon the credit programming project, homeworks (computer programs) and exam in laboratory.