It's true that Deep Belief Networks take a lot of work compared to, say, Random Forests, but they make good Neural Networks much easier. Before Deep Learning,

It's true that Deep Belief Networks take a lot of work compared to, say, Random Forests, but they make good Neural Networks much easier. Before Deep Learning, Neural Networks required a lot of hand tuning. Without that kind of tuning, Neural Networks with more than 1 or 2 hidden layers tended to perform very badly, because they would get stuck in plateaus or poor local optima. In contrast, (one facet of) Deep Learning focuses on using unsupervised learning to better initialize NNs, which in turn allows for much higher levels of accuracy. In essence, Deep Learning helps pick the NN parameters automatically. So it's actually decreasing the amount of hand tuning needed.