MicroGrad.jl: Part 4 Extensions
Read OriginalThis technical article, part 4 of a series on automatic differentiation in Julia, details extending the MicroGrad.jl library. It explains how to implement pullbacks for core functions like `map`, `getproperty`, and anonymous functions—which lack formal mathematical derivatives—to create a generic gradient descent optimizer. The tutorial demonstrates this by fitting a polynomial model, addressing challenges not covered by standard ChainRules.jl.
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