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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

  • TTM22SAI
    TTM22SAI
  • 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