Administrative Information
| Title | Convolutional Neural Networks |
| Duration | 60 |
| Module | B |
| Lesson Type | Lecture |
| Focus | Technical - Deep Learning |
| Topic | Deep learning |
Keywords
CNN,Python,Deep Learning,
Learning Goals
- To know what is a CNN and its main differences with Densely-connected NN
- To know the main difference between Locally-Connected Layers to Convolutional ones
- To know how to configure a CNN layer
- Pooling and batch normalization layers
- To know the most famous CNNs: LeNet, AlexNet, ResNet, VGG16, AllConvNet
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- Theory on Artificial Neural Networks
Optional for Students
- None
References and background for students
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. - Chapter 9
Recommended for Teachers
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. - Chapter 9
Lesson materials
Instructions for Teachers
This lecture will introduce students to the Convolutional Neural Networks (CNNs), explaining the main differences between classical Fully-Connected layers and Convolutional ones. The advantages of the weight sharing given by the Convolutional Layer are introduced and discussed, together with a comparison with the Locally-Connected layers. The Convolution operator is introduced, and kernel size, stride and padding are discussed as the main hyperparameters of a Convolutional layer. Then, Pooling and Batch Normalization layers will be introduced as part of several CNN architectures. To better undestand what a Convolutional layer has learned, possible ways to visualize learned filters will be introduced. Finally, an introduction to the most famous CNN architectures such as NetworkInNetwork and LeNet will be presented.
- Introduction to CNNs
- Main issues about fully-connected layers for high-dimensionality data
- Convolutional operator
- Description of a Convolutional layer in terms of neurons
- Convolutional layers
- Main properties of a Convolutional layer
- Local connectivity
- Weight sharing
- Convolutional layer hyperparameters
- Filter size
- Stride
- Padding
- Main properties of a Convolutional layer
- Pooling layers
- Visualizing layers
- Well-known architectures
- LeNet
- NiN
Time schedule
| Duration (min) | Description | Concepts | Activity | Material |
|---|---|---|---|---|
| 10 | Introduction to CNNs | |||
| 15 | Convolutional layers | |||
| 5 | Pooling layers | |||
| 15 | Visualizing layers | |||
| 15 | Well-known 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.