add char-rnn example
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@ -58,3 +58,30 @@ sample(model[1:end-1], 100)
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```
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```
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`sample` then produces a string of Shakespeare-like text. This won't produce great results after only a single epoch (though they will be recognisably different from the untrained model). Going for 30 epochs or so produces good results.
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`sample` then produces a string of Shakespeare-like text. This won't produce great results after only a single epoch (though they will be recognisably different from the untrained model). Going for 30 epochs or so produces good results.
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Trained on [a dataset from base Julia](https://gist.githubusercontent.com/MikeInnes/c2d11b57a58d7f2466b8013b88df1f1c/raw/4423f7cb07c71c80bd6458bb94f7bf5338403284/julia.jl), the network can produce code like:
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```julia
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function show(io::IO, md::Githompty)
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Buffer(jowerTriangular(inals[i], initabs_indices), characters, side, nextfloat(typeof(x)))
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isnull(r) && return
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start::I!
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for j = 1:length(b,1)
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a = s->cosvect(code)
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return
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end
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indsERenv | maximum(func,lsg))
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for i = 1:last(Abjelar) && fname (=== nothing)
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throw(ArgumentError("read is declave non-fast-a/remaining of not descride method names"))
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end
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if e.ht === Int
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# update file to a stroducative, but is decould.
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# xna i -GB =# [unsafe_color <c *has may num 20<11E 16/s
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tuple | Expr(:(UnitLowerTriangular(transpose,(repl.ptr)))
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dims = pipe_read(s,Int(a)...)
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ex,0 + y.uilid_func & find_finwprevend(msg,:2)
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ex = stage(c)
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# uvvalue begin
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end
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end
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```
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