Yota.jl is a package for reverse-mode automatic differentiation in Julia. The main features are:
- optimized for large inputs and conventional deep learning
- tracer-based with a hackable computational graph (tape)
- supports ChainRules API
Reverse-mode automatic differentiation in Julia
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dfdx/Yota.jl
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Reverse-mode automatic differentiation in Julia
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