I have been irritated that many recent introductions to machine learning/neural networks/whatever that fail to emphasise the most import trick in machine learning. Many internet resources don't mention it, and even good textbooks often don't drill it in to the reader the absolute criticality to success the trick is. In a machine learning context, we wish a learning system to generalise. That is, make good predictions on data it has never encounter before, based on what it learnt during from a training set. There is no easy formula to predict the ability of a learning system to generalise, but you can estimate it using held out data. That held out data is labelled but it is not used in training. It is called the validation set.
The first UK hacker camp in many years was taking place in Milton Keynes, hackers, makers and other interested people were going to be there from across the UK and beyond. It was an opportunity that couldn't be missed. After a early start the "hackbus" (a hired 9 seat mini-bus) was making good progress and we arrived by mid-afternoon in time to put up our tents and connect to the supper-fast Internet connection before the first talk started….
There were several other hack spaces, including NottingHack that had brought a gazebo village complete with laser cutter. Our "village" was slightly more modest a few straw bales with cooking equipment spread out nearby. A borrowed sail provided the only means of identification and protection from rain, that fortunately didn't occur in any significant quantity.