Deep learning 1 (5 cr)
Code: TT00CC66-3002
General information
Enrollment
01.12.2023 - 31.01.2024
Timing
01.01.2024 - 16.04.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Teknologia
Teaching languages
- Finnish
Degree programmes
- Bachelor’s Degree in Information and Communication Technology
Teachers
- Pekka Huttunen
Groups
-
TTM22SAITTM22SAI
- 09.01.2024 17:00 - 19:00, Syväoppiminen 1 TT00CC66-3002
- 23.01.2024 17:00 - 19:00, Syväoppiminen 1 TT00CC66-3002
- 06.02.2024 17:00 - 19:00, Syväoppiminen 1 TT00CC66-3002
- 20.02.2024 17:00 - 19:00, Syväoppiminen 1 TT00CC66-3002
- 19.03.2024 17:00 - 19:00, Syväoppiminen 1 TT00CC66-3002
- 02.04.2024 17:00 - 19:00, Syväoppiminen 1 TT00CC66-3002
- 16.04.2024 17:00 - 19:00, Syväoppiminen 1 TT00CC66-3002
Objective
The student understands the basics of deep learning and neural networks and the limitations and opportunities related to teaching them. The student can apply the methods used in deep learning in the Pytorch environment.
Content
- Artificial neurons and neural networks
- Deep learning with neural networks
- Teaching neural networks
- Use of trained neural networks
- Hyperparameters of neural networks
- Using the Pytorch environment
- CNN neural networks (Convolutional Neural Networks)
- RNN neural networks (Recurrent Neural Networks)
- Basics of natural language processing (NLP).
Evaluation scale
0 - 5
Prerequisites
Data science mathematics 1 (basic concepts of statistics)
Data science mathematics 2 (matrix algebra)
Python programming