Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Differentiable programming in machine learning
This paper explains automatic differentiation, discussing two primary modes - forward and backward - and their respective implementation methods. In the context of issues encountered in machine learning and deep learning, the forward mode is deemed more suitable as it efficiently differentiates functions with numerous inputs compared to outputs. Given Python's pivotal role in the ML landscape, the paper elaborates on two widely used deep learning libraries-PyTorch and TensorFlow. While both these libraries support automatic differentiation, they adopt distinct approaches, each carrying its unique strengths and weaknesses.
Differentiable programming in machine learning
This paper explains automatic differentiation, discussing two primary modes - forward and backward - and their respective implementation methods. In the context of issues encountered in machine learning and deep learning, the forward mode is deemed more suitable as it efficiently differentiates functions with numerous inputs compared to outputs. Given Python's pivotal role in the ML landscape, the paper elaborates on two widely used deep learning libraries-PyTorch and TensorFlow. While both these libraries support automatic differentiation, they adopt distinct approaches, each carrying its unique strengths and weaknesses.
Differentiable programming in machine learning
Kostić Marija A. (Autor:in) / Drašković Dražen D. (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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