Вернуться к Optimize TensorFlow Models For Deployment with TensorRT
Отзывы учащихся о курсе Optimize TensorFlow Models For Deployment with TensorRT от партнера Coursera Project Network
Оценки: 59
О курсе
This is a hands-on, guided project on optimizing your TensorFlow models for inference with NVIDIA's TensorRT. By the end of this 1.5 hour long project, you will be able to optimize Tensorflow models using the TensorFlow integration of NVIDIA's TensorRT (TF-TRT), use TF-TRT to optimize several deep learning models at FP32, FP16, and INT8 precision, and observe how tuning TF-TRT parameters affects performance and inference throughput.
Prerequisites:
In order to successfully complete this project, you should be competent in Python programming, understand deep learning and what inference is, and have experience building deep learning models in TensorFlow and its Keras API.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Лучшие рецензии
LS
3 июня 2021 г.
Great workshop, all the concepts were very well explained.
AA
14 мар. 2022 г.
The first to introduce such a rare and important topic.
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автор: Awais A
•28 мар. 2021 г.
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•1 окт. 2020 г.
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