Lecture: Building computational graphs, modern architectures

Lecture: Building computational graphs, modern architectures

Administrative information

Title Building computational graphs, modern architectures
Duration 60 min
Module B
Lesson Type Lecture
Focus Technical - Deep Learning
Topic

Computational Graphs

 

Keywords

neural networks, computational graph, residual connection, skip connection, deep learning,

 

Learning Goals

  • Understanding the fundamentals of Computational Graphs, residual connections, highway connections and skip connections

 

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

  • The Functional API
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778).
  • Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015). Highway networks. arXiv preprint arXiv:1505.00387.

Optional for Students

None.

References and background for students:

None.

Recommended for Teachers

None.

 

Lesson Materials

 

The materials of this learning event are available under CC BY-NC-SA 4.0.

 

Instructions for Teachers

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.

Outline

  • Computational graphs
  • Building computational graphs with Keras
  • Residual connections
  • Highway connections
  • Skip connections
Time schedule
Duration (Min) Description
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

 

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

Acknowledgements

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

 

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities

HCAIM Consortium

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    Laatst gewijzigd
    2024-05-15 11:12:28
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