François CholletVerified account

What's deep learning? The "common usage" definition as of 2019 would be "chains of differentiable parametric layers trained end-to-end with backprop". But this definition seems overly restrictive to me. It describes *how we do DL today*, not *what it is*.

If you have a convnet and you train its weights with ADMM, is that no longer deep learning? Is an HMAX model (with learned features) not deep learning? Is a deep neural network trained greedily layer-by-layer not deep learning? I say they're all deep learning.

This stands in contrast to: 1) Things that are not representation learning (e.g. manual feature engineering like SIFT, symbolic AI, etc.) 2) "Shallow learning", where there is a single feature extraction layer.

It does not prescribe a specific learning mechanism (e.g. backprop) or a specific use case (e.g. supervised learning or RL), and it does not require end-to-end joint learning (as opposed to greedy learning). It's the *what* (nature and structure), not the *how*.

This definition draws a clear boundary: some things are DL, some things aren't. The 2019 flavors of DNNs are DL, of course. So are DNNs trained with backprop alternatives like ES, ADMM, or virtual gradients. Genetic programming is not DL. Quicksort is not DL. Nor is SVM.

A single Dense layer is not DL. But a Dense stack is.l DL. K-means is not DL. But stacking k-means feature extractors is DL. When in 2011-12 I was doing stacked matrix factorization over matrices of pairwise mutual information of locations in video data, that was deep learning.

Programs typically written by human engineers are not DL. Parametrizing such programs to learn a few constants automatically is still not DL. You need to be doing representation learning with a chain of feature extractors.