deep learning

To learn is to adjust the parameters of an internal model. Learning to aim with one’s finger, for example, consists of setting the offset between vision and action: each aiming error provides useful information that allows one to reduce the gap. In artificial neural networks, although the number of settings is much larger, the logic is the same. Re

Deep learning is greedy. In order to set all the connections in a neural net correctly, deep learning often requires a massive amount of data.

guaranteed in “deeper” networks with more than two layers, it was possible to build a system that could produce results that were often good enough, opportunistically climbing up the mountain by taking small steps of the right sort, using a technique called backpropagation—now the workhorse of deep learning.*4 Backpropagation works by estimating th