[mlochbaum]

These are written in a generally basic and clean style (avoiding tacit programming which is sometimes considered hard to understand, e.g. function {s↑⍺↓⍵} instead of the train (s↑↓)). They're nice to read and I'd have no trouble maintaining them. You just don't know the language.

conv looks like the hardest. s←1+(⍴⍵)-⍴⍺ is the result shape, number of subarrays with the length of ⍺ that will fit in ⍵ in each dimension. Looks like (⍳⍴⍺){s↑⍺↓⍵}¨⊂⍵ is missing the ¨; the inner function s↑⍺↓⍵ drops ⍺ elements (left argument) and then takes the first s, so it gets a length-s window of ⍵. This is called on each possible index into ⍺, together with the whole of ⍵, so it ends up getting all such windows. Presumably s is expected to be larger than ⍴⍺ so this is the more efficient way to slice things. ⊃+/,⍺× multiplies by ⍺ and sums, ravelling with , before applying +/ to collapse the dimensions and sum them all at once. Each element is an array of shape s, so summing them gives a result of shape s. There you go, multidimensional convolution!

[lbatista]

Thanks for pointing the missing glyph. I will paste the correct code.

  backbias←{+/,⍵}
  logistic←{÷1+*-⍵}
  maxpos←{(,⍵)⍳⌈/,⍵}
  backavgpool←{2⌿2/⍵÷4}⍤2
  meansqerr←{÷∘2+/,(⍺-⍵)*2}
  avgpool←{÷∘4{+/,⍵}⌺(2 2⍴2)⍤2⊢⍵}
  conv←{s←1+(⍴⍵)-⍴⍺⋄⊃+/,⍺×(⍳⍴⍺){s↑⍺↓⍵}¨⊂⍵}
  backin←{(d w in)←⍵⋄⊃+/,w{(⍴in)↑(-⍵+⍴d)↑⍺×d}¨⍳⍴w}
  multiconv←{(a ws bs)←⍵⋄bs{⍺+⍵ conv a}⍤(0,(⍴⍴a))⊢ws}