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README.md

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@@ -24,7 +24,7 @@ You can choose to mix and match loss functions natively found in this package an
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In such cases, you optimize over a sum of different objectives (e.g. ML + Ridge).
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This mix and match strategy also applies to gradients, where you may supply analytic gradients or opt for automatic differentiation or mix analytical and automatic differentiation.
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# you may consider using this package if:
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# You may consider using this package if:
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- you want to extend SEM (e.g. add a new objective function) and need an extendable framework
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- you want to extend SEM, and your implementation needs to be fast (because you want to do a simulation, for example)

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