Title | Building computational graphs, modern architectures | ![]() |
Duration | 60 min | |
Module | B | |
Lesson Type | Lecture | |
Focus | Technical - Deep Learning | |
Topic |
Computational Graphs |
neural networks, computational graph, residual connection, skip connection, deep learning,
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The materials of this learning event are available under CC BY-NC-SA 4.0.
In this lecture one of the main goals is to show, that deep neural networks are computational graphs, that can scale well. In the lecture modern architectures, including residual, highway and skip connections, that are widely applied in neural networks are introduced.
Duration (Min) | Description |
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10 | Introduction to computational graphs of neural networks |
15 | Introduction of functional API of Keras with an example |
10 | Description of residual connections with example source code |
10 | Description of highway connections with example source code |
10 | Description of skip connections with example source code |
5 | Summary and conclusions |
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
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The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068. The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities |