Regularization Techniques
| Title | Regularization Techniques | |
| Duration | 60 min | |
| Module | B | |
| Lesson Type | Lecture | |
| Focus | Technical - Deep Learning | |
| Topic |
Regularization Techniques |
Regularization, Callbacks, Gridsearch,
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The materials of this learning event are available under CC BY-NC-SA 4.0.
This lecture will introduce students to the fundamentals of the hyperparameter tuning. We will use the Census Dataset as the examples of the use and outcomes from the regularisation techniques. The Adult Census dataset is a binary classification problem. The goal of this lecture is to introduce several forms of regularisation, starting with weight initialisers, bias, co-adaption, callbacks, a grid search for automatic hyperparameter tunning, and additional regularisation checking techniques. The goal is to identify techniques to support the development of generalisable models with limited co-adaption to learn the function and not the data. Some of these techniques also improve training time, thus can reduce the computation needed for larger models.
| Duration (Min) | Description |
|---|---|
| 10 | Weight initialisers and bias |
| 10 | Co-adaption |
| 10 | Callbacks |
| 20 | GridSearch |
| 10 | Additional checks |
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
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