jessebett

I'm not sure what `inspect` does, but can you clarify what you mean by proper pipes?

  julia> x = [3,2,4,5]
  4-element Array{Int64,1}:
   3
   2
   4
   5

  julia> x |> sort |> x->reshape(x,(2,2))
  2×2 Array{Int64,2}:
   2  4
   3  5

works.

It gives a syntax error if the pipes are on the newline. Is that your gripe?

Or is it that it doesn't automatically partially apply the function, requiring you to use an anonymous function, like `x->reshape(x,(2,2))` instead of as you have it `resize(100,100)` implicitly. I prefer the explicitness.

I've encountered a few pieces of math that are more naturally expressed in 0-based indexes and it is pretty easy to define them in Julia using arbitrary indexing.

TaylorSeries.jl is a great example of this. x = x_0 + x_1 t + ... Then indexing into it with x[0] would give the zero-th order Taylor coefficient.

As with all science communication, it is highly specific to the audience.

I would say if you are completely unfamiliar with machine learning then this article accomplishes the job of informing you that we've proposed something exciting and a bit different, and gives some flavour for what kind of problems we're interested in (like continuous time medical data).

However, this article assumes essentially zero technical background. So if you're comfortable with concepts either from machine learning (at the level of vaguely knowing some details about a neural network) or mathematics (taken some calculus or know about differential equations) then you will probably find this article lacking in details. Again, this is just that you're not in the target audience.

If you don't know where you fall on that spectrum of prerequisite background I highly recommend taking a look at some of David's responses in the thread I linked where he gives a pretty good quick primer with a bit more technical detail.