Lecture: Neural Networks

Lecture: Neural Networks

Administrative information

Title Neural Networks
Duration 60
Module A
Lesson Type Lecture
Focus Practical - AI Modelling
Topic AI - Modelling

 

Keywords

Neural network,backpropagation,optimization,

 

Learning Goals

  • Learners understand the MLP architectre
  • Learners can construct neural networks with fully connected layers and various activation functions
  • Learners understand the basic idea of backpropagation and gradient-based methods

 

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

  • Review of linear algebra and vector calculus.

Optional for Students

None.

References and background for students:

Recommended for Teachers

  • Familiarize themselves with the demonstration materials.

Lesson Materials

 

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

 

Instructions for Teachers

Cover the topics in the lesson outline and demonstrate the concepts using the interactive notebooks (shape of the loss function w.r.t. different regularizers, gradient-based optimization algorithms). Give a brief overview of the code.

Outline/time schedule

 
Duration (min) Description Concepts
5 From logistic regression to perceptron input, weights, bias, sigmoid function
10 Multilayer perceptron and matrix multiplications input layer, hidden layer, output layer
20 Derivation of the backpropagation scheme gradient descent, learning rate, backpropagation
10 Activation functions ReLU, sigmoid, tanh, softmax etc.
10 Loss functions for classification and regression MSE, binary and categorical cross-entropy
5 Demonstration  

 

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

Colofon

Het arrangement Lecture: Neural Networks is gemaakt met Wikiwijs van Kennisnet. Wikiwijs is hét onderwijsplatform waar je leermiddelen zoekt, maakt en deelt.

Auteur
HCAIM Consortium
Laatst gewijzigd
2024-05-15 11:03:55
Licentie

Dit lesmateriaal is gepubliceerd onder de Creative Commons Naamsvermelding-GelijkDelen 4.0 Internationale licentie. Dit houdt in dat je onder de voorwaarde van naamsvermelding en publicatie onder dezelfde licentie vrij bent om:

  • het werk te delen - te kopiëren, te verspreiden en door te geven via elk medium of bestandsformaat
  • het werk te bewerken - te remixen, te veranderen en afgeleide werken te maken
  • voor alle doeleinden, inclusief commerciële doeleinden.

Meer informatie over de CC Naamsvermelding-GelijkDelen 4.0 Internationale licentie.

Aanvullende informatie over dit lesmateriaal

Van dit lesmateriaal is de volgende aanvullende informatie beschikbaar:

Toelichting
.
Eindgebruiker
leerling/student
Moeilijkheidsgraad
gemiddeld
Studiebelasting
4 uur en 0 minuten

Gebruikte Wikiwijs Arrangementen

HCAIM Consortium. (z.d.).

Acknowledgement

https://maken.wikiwijs.nl/198386/Acknowledgement

HCAIM Consortium. (z.d.).

Lecture: Duty Ethics

https://maken.wikiwijs.nl/198966/Lecture__Duty_Ethics

close
Colofon
thumb  
gemaakt met Wikiwijs van kennisnet-logo
Lecture: Neural Networks
open