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Why TensorFlow eager execution matters - Sigrid Keydana

Sigrid Keydana
Applied Researcher at RStudio, Inc.
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RStudio Conference 2019
January 17 2019, Texas, United States
RStudio Conference 2019
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Why TensorFlow eager execution matters - Sigrid Keydana
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Passionate about deep learning, machine learning & statistics, R, Linux and functional programming.

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In current deep learning with Keras and TensorFlow, when you've mastered the basics and are ready to dive into more involved applications (such as generative networks, sequence-to-sequence or attention mechanisms), you may find that surprisingly, the learning curve doesn't get much flatter. This is largely due to restrictions imposed by TensorFlow's traditional static graph paradigm. With TensorFlow Eager Execution, available since summer and announced to be the default mode in the upcoming major release, model architectures become more flexible, readable, composable, and last not least, debuggable. In this session, we'll see how with Eager, we can code sophisticated architectures like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in a straightforward way.

VIEW MATERIALS https://github.com/skeydan/rstudio_conf_2019_eager_execution

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