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People sometimes use 1st layer, 2nd layer to refer to a specific layer in a neural net. Is the layer immediately follows the input layer called 1st layer?

How about the lowest layer and highest layer?

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    $\begingroup$ "Deep learning" may suggest vertical terminology, but everyone seems to draw left-to-right (horizontal) networks. Perhaps we should rename it to wide learning ;) $\endgroup$ – MSalters Apr 28 '20 at 9:22
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People sometimes use 1st layer, 2nd layer to refer to a specific layer in a neural net. Is the layer immediately follows the input layer called 1st layer?

The 1st layer should typically refer to the layer that comes after the input layer. Similarly, the 2nd layer should refer to the layer that comes after the 1st layer, and so on.

However, note that this convention and terminology may not be applicable in all cases. You should always take into account your context!

How about lowest layer and highest layer?

To be honest, I also dislike this ambiguous terminology. From my experience, I don't think there's an agreement on the actual meaning of "lowest" or "highest". It depends on how you depict the neural network, but it's possible that "lowest" refers to the layers closer to the inputs, because, if you think of a neural network as a hierarchy that starts from the inputs and builds more complex representations of it, the "lowest" may refer to the "lowest in the hierarchy" (but who knows!).

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Lowest layer generally refers to the layer closest to the input. This comes from the idea that layers closer to the input represent low-level features such as gradients and edges, while layers closer to the output represent high-level features such as parts and objects.

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  • $\begingroup$ You will also see the term "TOP" layer. This usually refers to the output layer of the network. $\endgroup$ – Gerry P Apr 28 '20 at 3:52

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