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What's New In TensorFlow 2.x? /
Since its 2015 release, TensorFlow has become a de facto standard among enterprise AI technologies. This comprehensive report introduces the new features of TensorFlow 2.x and Keras to developers and data scientists with machine learning skills. Many companies consider TensorFlow 2.x to be a major s...
| Autores principales: | , |
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| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
O'Reilly Media, Inc.,
2020.
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a Since its 2015 release, TensorFlow has become a de facto standard among enterprise AI technologies. This comprehensive report introduces the new features of TensorFlow 2.x and Keras to developers and data scientists with machine learning skills. Many companies consider TensorFlow 2.x to be a major step in building a one-stop shop for deep learning tasks they need to perform. Romeo Kienzler, chief data scientist at the IBM Center for Open Source Data and AI Technologies, and Jerome Nilmeier, a data scientist and developer at Spark Technology Center, explain how your company can benefit from the new TensorFlow functionality. After reading this report, you can determine objectively whether adopting or upgrading to TensorFlow 2.x is worth your while. You'll examine: Two key TensorFlow APIs: Tensor-based API and Keras The eager execution mode for natural Python programming without TensorFlow sessions tf.function and AutoGraph for creating TensorFlow code in pure Python that the TensorFlow execution engine can consume How Keras is now tightly integrated with the TensorFlow backend under the hood The TensorBoard visualization framework, which contains rich capabilities How TensorFlow accomplishes parallel neural network training and scoring TensorFlow 2.x features including API cleanup, improved model export, and TensorFlow Serving. | ||
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