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Differentiable programming

Programming paradigm in which a numeric computer program can be differentiated throughout via automatic differentiation, allowing for machine learning based on gradient descent etc.

Differentiable programming is a programming paradigm in which a numeric computer program can be differentiated throughout via automatic differentiation. This allows for gradient-based optimization of parameters in the program, often via gradient descent, as well as other learning approaches that are based on higher-order derivative information.

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Differentiable programming

Programming paradigm in which a numeric computer program can be differentiated throughout via automatic differentiation, allowing for machine learning based on gradient descent etc.

Texto en inglés

Differentiable programming is a programming paradigm in which a numeric computer program can be differentiated throughout via automatic differentiation. This allows for gradient-based optimization of parameters in the program, often via gradient descent, as well as other learning approaches that are based on higher-order derivative information.

En Wikipedia

Texto en inglés Aún no hay artículo en tu idioma: extracto en inglés.

Differentiable programming is a programming paradigm in which a numeric computer program can be differentiated throughout via automatic differentiation. This allows for gradient-based optimization of parameters in the program, often via gradient descent, as well as other learning approaches that are based on higher-order derivative information. Differentiable programming has found use in a wide variety of areas, particularly scientific computing and machine learning. One of the early proposals to adopt such a framework in a systematic fashion to improve upon learning algorithms was made by the Advanced Concepts Team at the European Space Agency in early 2016.

Texto: Wikipedia en inglés, CC BY-SA 4.0. ·

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