Date: October 7, 2016
- 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.
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