Advanced Deployment Scenarios with TensorFlow

开始时间: 05/23/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 其他类别

大学或机构: CourseraNew

   

课程主页: https://www.coursera.org/learn/advanced-deployment-scenarios-tensorflow

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Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this final course, you’ll explore four different scenarios you’ll encounter when deploying models. You’ll be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You’ll move on to TensorFlow Hub, a repository of models that you can use for transfer learning. Then you’ll use TensorBoard to evaluate and understand how your models work, as well as share your model metadata with others. Finally, you’ll explore federated learning and how you can retrain deployed models with user data while maintaining data privacy. This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

TensorFlow的高级部署场景:将机器学习模型带入现实世界不仅涉及建模,还涉及很多其他内容。本专业知识将教您如何导航各种部署方案并更有效地使用数据来训练模型。 在这最后的课程中,您将探索在部署模型时会遇到的四种不同情况。将向您介绍TensorFlow Serving,该技术可让您通过网络进行推理。您将转到TensorFlow Hub,该模型库可用于转移学习。然后,您将使用TensorBoard评估和了解模型的工作方式,并与他人共享模型元数据。最后,您将探索联合学习,以及如何在保持数据隐私的同时使用用户数据重新训练已部署的模型。 该专业化基于我们的TensorFlow实践专业化。如果您不熟悉TensorFlow,我们建议您首先参加TensorFlow实践专业化课程。为了对神经网络的工作方式有更深入的基础了解,我们建议您参加“深度学习专业化”课程。

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