Administrative information


 

Title Regularization Techniques
Duration 60 min
Module B
Lesson Type Tutorial
Focus Technical - Deep Learning
Topic Regularization Techniques

 

Keywords


Regularization, Callbacks, Gridsearch,

 

Learning Goals


 

Expected Preparation


Learning Events to be Completed Before

None.

Obligatory for Students

None.

Optional for Students

None.

References and background for students:

  •  
  • John D Kelleher and Brain McNamee. (2018), Fundamentals of Machine Learning for Predictive Data Analytics, MIT Press.
  • Michael Nielsen. (2015), Neural Networks and Deep Learning, 1. Determination press, San Francisco CA USA.
  • Charu C. Aggarwal. (2018), Neural Networks and Deep Learning, 1. Springer
  • Antonio Gulli,Sujit Pal. Deep Learning with Keras, Packt, [ISBN: 9781787128422].

Recommended for Teachers

None.

Lesson Materials


 


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

 

Outline

Time schedule
Duration (Min) Description
10 Providing an overview of the practical and importing datasets with the basic pre-processing
10 Models to explore topologies
20 Hyperparameter investigation with regularisation techniques
10 Grid search (note this should be pre done - either by lecturer or students in flipped mode as it can take significant time to run live)
5 Final model discussion

 

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