ANN Exercise 2

The second exercise for artificial neural networks. The associated code is in the ANN Ex2.R file. Interested students are encouraged to replicate what we go through in the video themselves in R, but note that this is an optional activity intended for those who want practical experience in R and machine learning.

In this exercise, we perform a hyper-parameter search, where we are seeking to discover (i) a good number of hidden nodes; and (ii) what a good value would be for our L2 regularization parameter. The method we employ is grid search, and we proceed so as to minimize the effect of the non-determinism present in the ANN optimization process.

We will be using the well known Iris dataset, which means this is a classification problem. We discuss the specifics of this problem, and in particular the effect of the fact that our data is so small and that many of our models performed equally well on the validation data.

Note that the utils, nnet and datasets R packages are used in this exercise. You will need to have them installed on your system. You can install packages using the install.packages function in R.

Please note that the audio quality on parts of this video are of lower quality that other videos in this course.

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This video is from the free online course:

Advanced Machine Learning

The Open University