Administrative information

 

Title Forward propagation
Duration 60 min
Module B
Lesson Type Tutorial
Focus Technical - Deep Learning
Topic Forward pass

 

Keywords

Forward pass,Loss,

 

Learning Goals

 

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

None.

Optional for Students

  • Matrices multiplication
  • Getting started with Numpy
  • Knowledge of linear and logistic regression ([Lecture: Linear Regression]

References and background for students:

  • John D Kelleher and Brain McNamee. (2018), Fundamentals of Machine Learning for Predictive Data Analytics, MIT Press.
  • Michael Nielsen. (2015), Neural Networks and Deep Learning, 1. Determination press, San Francisco CA USA.
  • Charu C. Aggarwal. (2018), Neural Networks and Deep Learning, 1. Springer
  • Antonio Gulli,Sujit Pal. Deep Learning with Keras, Packt, [ISBN: 9781787128422].

Recommended for Teachers

None.

Lesson Materials

 

The materials of this learning event are available under CC BY-NC-SA 4.0.

 

Instructions for Teachers

Here the goal is to show the implementation steps of the transformer network for images. We also implement a basic CNN with a similar number of trainable parameters, so we can compare the performance of the CNN to the ViT model.

Outline

Duration (min) Description
15 Training a simple convolutional neural network
35 Training a vision transformer (ViT) neural network
10 Comparing the performance of the two different approaches

 

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