Carlo Lucibello

I find convenient the following methods: ```julia onehot(x) = onehot(Int, x) onehot(T::Type, x) = convertlabel(LabelEnc.OneOfK{T}, x) ``` Worth the addition?

Floops depedency should be removed

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[FLoops](https://github.com/JuliaFolds/FLoops.jl) is no longer mantained and it has not been forked yet by the JuliaFolds2 organization (see https://github.com/JuliaFolds2/Transducers.jl/issues/31) What are the available alternatives for our DataLoader implementation? Would the simple...

use buffer in BatchView

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fix #156 This covers only `BatchView(...; collate=nothing)`, I'm not sure how `collate=true` and `collate=false` should interact with the buffer so I won't address that here.

In order to facilitate non-mutating and AD-friendly array manipulation. See https://github.com/FluxML/Zygote.jl/issues/377#issuecomment-547703073

with similar behavior to ones_like and zeros_like

so that `flatten(x, dims=1:2)` flattens the first two dimensions and so on.

In particular: - tutorials - group the docstrings in some categories (e.g. partitioning, grouping, transforming, etc...) Some documentation can be ported from MLDatapattern (see #115)

Status of MLDataPattern porting

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A list of what is currently exported from MLDataPattern.jl. ### TO PORT - [x] `getobs`, `getobs!` and `nobs`. #1 - `nobs` is now `numobs`; - `obsdim` argument is dropped from...

With tuples: ```julia julia> t = (zeros(3), ones(3)) ([0.0, 0.0, 0.0], [1.0, 1.0, 1.0]) julia> m = mapobs(x -> (x[1], 2*x[2]), t) mapobs(#15, Tuple{Vector{Float64}, Vector{Float64}}) julia> m[1] (0.0, 2.0) julia>...