Title | Convolutional Neural Networks | ![]() |
Duration | 60 min | |
Module | B | |
Lesson Type | Lecture | |
Focus | Technical - Deep Learning | |
Topic |
Deep learning |
CNN,Python,Deep Learning,
The materials of this learning event are available under CC BY-NC-SA 4.0.
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.
Duration (min) | Description | Concepts | Activity | Material |
---|---|---|---|---|
10 | Introduction to CNNs | |||
15 | Convolutional layers | |||
5 | Pooling layers | |||
15 | Visualizing layers | |||
15 | Well-known architectures |
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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 |