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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.
By definition, deep learning is a gradual, incremental way to extract representations from data. In its modern incarnation, it's even at least C1 continuous (more typically C inf). That last part isn't essential, but *incrementality* is intrinsic to DL.
So DL is a fundamentally different beast from symbol manipulation and regular programming, which is fundamentally discrete, flow-centric, and doesn't usually involve intermediate data representations. You could do symbol manipulation with DL, but it involves lots of extra steps.
These are two entirely different takes on data manipulation. Deep learning isn't just end-to-end gradient descent, but not every program is deep learning either. In fact, deep learning models only represents a tiny, tiny slice of program space. It can't hurt to look beyond it.
interesting because structured composition and high reusability always seemed to me to be a core aspect of both (human enginnered programs/DL)
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