Transformation, offers a midpoint between these two library design patterns,Ĭapturing the benefits of both. Weĭescribe how the use of staged programming in Python, via source code TensorFlow and Theano benefit from whole-program optimization and can beĭeployed broadly, but make expressing complex models more cumbersome. To write, but suffer from high interpretive overhead and are not easilyĭeployable in production or mobile settings. Machine learning, imperative style libraries like Autograd and PyTorch are easy Write, and machine learning code that is scalable or fast to execute. Authors: Dan Moldovan, James M Decker, Fei Wang, Andrew A Johnson, Brian K Lee, Zachary Nado, D Sculley, Tiark Rompf, Alexander B Wiltschko Download PDF Abstract: There is a perceived trade-off between machine learning code that is easy to
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