Administrative Information
| Title | Convolutional Neural Networks |
| Duration | 60 |
| Module | B |
| Lesson Type | Tutorial |
| Focus | Technical - Deep Learning |
| Topic | Deep learning |
Keywords
CNN,Deep Learning,Python,
Learning Goals
- Implementing and training a CNN for an image classification problem from scratch
- Fine-tuning of an already trained network
- Transfer learning using architectures trained on ImageNet
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- Theory on CNN
Optional for Students
- None
References and background for students
- None
Recommended for Teachers
None.
Lesson materials
Instructions for Teachers
This Tutorial covers fundamental CNN development, training and testing. Three different tutorial implemented in form of Jupyter Notebook will be shown and discussed. In particular:
- the implementation of a simple CNN will be shown. The training will be made with a simple freely available dataset (e.g. MNIST). Evaluation in terms of accuracy of a test set after the training stage will be shown.
- the fine tuning of an already trained network will be made on a new dataset (e.g., Fashion-MNIST). Evaluation and a comparison with a network trained from scratch will be shown and discussed.
- how to load and save custom models will be shown.
Time schedule
| Duration (min) | Description | Concepts | Activity | Material |
|---|---|---|---|---|
| 20 | Implementing and training a simple CNN | |||
| 20 | Fine tuning of an already trained network | |||
| 20 | load and save architectures |
Acknowledgements
We thank Eng. Andrea Apicella for his contribution in developing the material.
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.