Deep Gold: Using Convolution Networks to Find Minerals

Date: October 7, 2016 Published by

  • We haven’t over sampled the gold examples in the testing data, so they contain a lot more not gold than gold.
  • Here’s what a false colour image ready to feed into our convolution network looks like:

    Convolution networks have shown amazing ability to learn answers to visual problems.

  • We can make use of Logistic classification, Support Vector Machine classification, Naive Bayes classification, Random Forests and Multilayer Perceptron classification with just a few lines of code.
  • So I found some papers written on finding minerals (gold in particular) using images.
  • So we didn’t really find gold, but I’m still pleasantly surprised by the accuracy of convolution network features on such a different problem to that which they were trained.