Administrative information
| Title |
Convolutional Neural Networks |
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| Duration |
60 min |
| 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
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
The materials of this learning event are available under CC BY-NC-SA 4.0.
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
- Pooling layers
- Visualizing layers
- Well-known architectures
Time schedule
| Duration (min) |
Description |
Concepts |
Activity |
Material |
| 10 |
Introduction to CNNs |
|
|
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| 15 |
Convolutional layers |
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|
|
| 5 |
Pooling layers |
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|
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| 15 |
Visualizing layers |
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|
|
| 15 |
Well-known architectures |
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|
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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
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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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