Administrative information


 

Title Derivation and application of backpropagation
Duration 60 min
Module B
Lesson Type Tutorial
Focus Technical - Deep Learning
Topic Deriving and Implementing Backpropagation

 

Keywords


Backpropagation,activation functions,deviation,

 

Learning Goals


 

Expected Preparation


Obligatory for Students

  • Calculus revision (derivatives, partial derivatives, the Chain rule)

Optional for Students

None.

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


 

Outline


Time schedule
Duration (Min) Description
20 (Optional) Problem 1: derivation of the backpropagation formula using the Sigmoid function for the inner and outer activation functions and MSE as the loss function (Optional)
20 Problem 2: Students will apply three activation functions for a single weight update (SGD backpropagation), using pen and paper for (20 Minutes):
20 Problem 3: Students will develop a neural network from scratch using only the Numpy module, where the user can select from any of three hidden layer activation functions where the code can preform backpropagation
10 Problem 4: Students will using the Tensorflow 2.X module with the inbuild Keras module, preform backpropagation using SGD.
10 Recap on the forward pass process

 

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