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Basically just neural networks. The significant difference is that in the past neural networks were "shallow" and only had one layer (as were most other machin

Basically just neural networks. The significant difference is that in the past neural networks were "shallow" and only had one layer (as were most other machine learning algorithms.) Now people are building neural networks with hundreds of layers and millions of parameters. If you aren't familiar with neural networks, I think this is a good introduction that goes into a lot of detail: https://karpathy.github.io/neuralnets/ https://karpathy.github.io/neuralnets/

radarsat1 11y ago
This is the usual definition, assuming NN, because it's a conveniently general model with a well-understood training method. However, to me, deep learning is a more general concept: several layers to learn features, with a final classification or regression layer; where "layer" is some statistical learning process. The key idea is that features can be learned in a hierarchical way, not so much that they are NN layers, which is an implementation detail. For example, several layers of high-dimensional k-means unsupervised learning followed by a simple linear classification stage can perform very well.