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@ -32,8 +32,6 @@ julia> gradient(f, [2, 1], [2, 0])
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But machine learning models can have *hundreds* of parameters! To handle this, Flux lets you work with collections of parameters, via `params`. You can get the gradient of all parameters used in a program without explicitly passing them in.
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But machine learning models can have *hundreds* of parameters! To handle this, Flux lets you work with collections of parameters, via `params`. You can get the gradient of all parameters used in a program without explicitly passing them in.
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```jldoctest basics
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```jldoctest basics
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julia> using Flux
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julia> x = [2, 1];
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julia> x = [2, 1];
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julia> y = [2, 0];
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julia> y = [2, 0];
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