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- W3207271311 abstract "Machine learning and neural network models in particular have been improving the state of the art performance on many artificial intelligence related tasks. Neural network models are typically implemented using frameworks that perform gradient based optimization methods to fit a model to a dataset. These frameworks use a technique of calculating derivatives called automatic differentiation (AD) which removes the burden of performing derivative calculations from the model designer. In this report we describe AD, its motivations, and different implementation approaches. We briefly describe dataflow programming as it relates to AD. Lastly, we present example programs that are implemented with Tensorflow and PyTorch, which are two commonly used AD frameworks." @default.
- W3207271311 created "2021-10-25" @default.
- W3207271311 creator A5019304909 @default.
- W3207271311 date "2021-10-17" @default.
- W3207271311 modified "2023-09-27" @default.
- W3207271311 title "A Brief Introduction to Automatic Differentiation for Machine Learning." @default.
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