Administrative information


 

Title Hyperparameter tuning
Duration 60 min
Module B
Lesson Type Tutorial
Focus Technical - Deep Learning
Topic Hyperparameter tuning

 

Keywords


Hyperparameter tuning,activation functions,loss, epochs, batch size,

 

Learning Goals


 

Expected Preparation


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.

 

Instructions for Teachers

 

 

 

Outline


Time schedule
Duration (Min) Description
5 Pre-processing the data
10 Capacity and depth tunning (under and over fitting)
10 Epochs (under and over training)
10 Batch sizes (for noise suppression)
10 Activation functions (and their effects on performance - time and accuracy)
10 Learning rates (vanilla, LR Decay, Momentum, Adaptive)
5 Recap on some staple hyperparameters (ReLu, Adam) and the tunning of others (capacity and depth).

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