I think I have a pretty good grasp on the meaning and scope of 'Machine Learning' but less so on the emerging field of 'Deep Learning'. Tomasz Malisiewicz has both the background and perspective to put these terms in context for us and I enjoyed his clear explanation. You can see it here:
Deep Learning vs Machine Learning vs Pattern Recognition
Comment
Relevant:
"A Common Logic to Seeing Cats and Cosmos"
https://www.quantamagazine.org/20141204-a-common-logic-to-seeing-ca...
Function Compositional Mapping:
https://en.wikipedia.org/wiki/Function_composition
In Electrical/Electronic Engineering, they don't call it learning. They call it System Identification. In machine learning they call it learning. Different terminologies from different domains & yet they mean the same thing mathematically and that is functional mapping.
In mathematical terminology they all lumped into functional mapping, whether its machine learning, deep learning, pattern recognition, system-identifications, etc, etc.
One maps X into Y via an approximating functional mapping function f , ie, Y:--> f(X)
Too many throw around terminologies these days without practitioners understanding of what they mean.
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