Hands on with neural-network toolkit LIME: Come now, you sourpuss. You've got some explaining to do

Deep learning has become the go-to "AI" technique for image recognition and classification. It has reached a stage where a programmer doesn't even have to create their own models, thanks to a large number available off the shelf, pre-trained and ready for download. Training these models is essentially an optimisation exercise, something that involves some complicated (well, relatively) maths in order to reduce the number of errors on each cycle by adjusting a large number of internal weighting factors. At the end of the training phase, you should have a neural net that can accomplish the task it has been assigned. The problem with neural nets (and deep learning) is that once they have been trained, we don't know what's going on inside them.