Lecturer: Artem Chernodub
Academic group: PKN23/M
Year of study: 1

"Introduction to Deep Learning" aims to provide an overview of general DL design principles and the most common applications. Mainstream DL approaches, methods, and applications are considered and discussed. The course contains two homework assignments. Each assignment includes theoretical and practical tasks. In the theoretical part, you must write mathematical equations describing the neural network's forward and backward passes. The outcome of the valuable part is code written in Python: you will be asked to develop the neural network "from scratch."

The goal of this course is to equip participants with the essential skills and knowledge needed to excel in various communication contexts, ranging from personal interactions to corporate settings. Divided into five comprehensive modules, the course explores the intricacies of effective communication, group dynamics, public speaking, corporate communications, and academic discourse. Participants will have the opportunity to develop key skills such as assertiveness, persuasion, negotiation, and crisis management. As a result, they will be prepared for confident and successful communication in any environment.

Lecturer: Oleksii Ignatenko
Academic group: PKN23/M
Year of study: 1

Lecturer: Roman Nazarenko
Academic group: PKN23/M
Year of study: 1

Lecturer: Roman Kyrychenko
Academic group: PKN23/M
Year of study: 1

Lecturer: Yurii Kaminskii
Academic group: PKN23/M
Year of study: 1

Lecturer: Ivan Petrenko
Academic group: PKN23/M
Year of study: 1

Lecturer: Oleksandr Romanko
Academic group: PKN23/M
Year of study: 1

Lecturer: Yarema Ohrin
Academic group: PKN23/M
Year of study: 1

Lecturer: Rostyslav Hryniv
Academic group: PKN23/M
Year of study: 1