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Umeå University
Deep Learning
The course is about neural networks and gives an introduction to the field of deep learning. The content includes the components used to construct deep neural networks, e.g., activation functions, loss functions, regularization techniques (e.g., normalization and dropout), optimization methods (specifically variants…
- Higher education
- Information unavailable
- 25 March 2027
- Information unavailable
- Information unavailable
- 50 %
Overview
The course is about neural networks and gives an introduction to the field of deep learning. The content includes the components used to construct deep neural networks, e.g., activation functions, loss functions, regularization techniques (e.g., normalization and dropout), optimization methods (specifically variants of stochastic gradient descent), network architectures. Also covered is deep generative models. The students learn to apply their knowledge by implementing and training modern network architectures and deep learning methods on large data sets. The course is split into two modules: Theory, 5.5 credits Laboration, 2.0 credits
Admission scores
Entry requirements
At least 90 ECTS including at least 60 ECTS computing science, or at least 120 ECTS within a study programme. At least 7.5 ECTS programming; 7.5 ECTS data structures and algorithms; 7.5 ECTS linear algebra; 7.5 ECTS mathematical analysis (predominantly differential calculus); 7.5 ECTS mathematical statistics and probability theory; 7.5 ECTS machine learning. Proficiency in English equivalent to the level required for basic eligibility for higher studies.
The text is reproduced from the Susa source. Antagningsdata does not map GY11 and GY25 or assess personal eligibility.
Source, measure and data quality
- Source
- Skolverket Susa-navet
- Period
- 2027-03-25
- Measure
- Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
- Population
- Education offering e.uoh.umu.5dv236.a5703.20271
- Last checked
- 2026-09-23T10:39:35.037285+00:00
- Limitation
- Antagningsdata does not map GY11 and GY25. General and specific conditions are not separated without structured source data.
Programme content
Study structure
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- Programme or course starts
- Programme or course ends
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About the provider
Sources and data quality
Education facts for the selected offering come from Skolverket Susa-navet.
Retrieved . Published . Times are shown in Swedish local time.
Source identity and publication version
- Publication version
- 8e217193-f5fa-4778-b085-a4521fd03e8d
- Education identity in the source
- i.uoh.umu.5dv236.a5703.20271
- Offering identity in the source
- e.uoh.umu.5dv236.a5703.20271
- Education-form source code
- HS
- Education code in the source
- 5DV236
- Change time according to the source
- 2025-12-11T08:04:31
The provider, education and education offering are separate identities. Application information should be checked on the official website. Supplementary statistics have not been obtained from this source.