What is the "dropout" technique? What purpose does the "dropout" method serve and how does it improve the overall performance of the neural network?
Dropout means that every individual data point is only used to fit a random subset of the neurons. This is done to make the neural network more like an ensemble model.

That is, just as a random forest is averaging together the results of many individual decision trees, you can see a neural network trained using dropout as averaging together the results of many individual neural networks (with 'results' understood to mean activations at every layer, rather than just the output layer).