Lecture: Convolutional Neural Networks

Lecture: Convolutional Neural Networks

Administrative information

Title Convolutional Neural Networks
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

  • None

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


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
    • 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      

 

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

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

 

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities

HCAIM Consortium