Nematrian Reference Library
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| Reference | Title | Link |
| Baydin, A.G., Pearlmutter B., Radul, A.A. and Siskind, J.M. (2018) | Automatic Differentiation in Machine Learning: a Survey | here |
Abstract (partial)
"Derivatives, mostly in the form of gradients and Hessians, are ubiquitious in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply "autodiff", is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs ..." |
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