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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/advanced-deployment-scenarios-tensorflow
课程评论：没有评论
课程摘要：高级TensorFlow部署场景 本课程名为“高级部署场景与TensorFlow”。它侧重于将机器学习模型融入现实世界的复杂性，远不止于建模。该专业将教您如何有效应对多种部署场景，并更好地利用数据以训练模型。 在本课程的最后阶段，您将探索在部署模型时可能遇到的四种不同场景。您将学习TensorFlow Serving，这是一种允许您通过网络进行推断的技术。接下来，您将接触TensorFlow Hub，这是一个可用于迁移学习的模型库。然后，您将使用TensorBoard来评估和理解模型的工作原理，并与他人共享模型元数据。最后，您将探索联邦学习，了解如何在保持数据隐私的同时使用用户数据来重新训练已部署的模型。 本专业建立在“TensorFlow实践专业”基础上。如果您是TensorFlow的新手，我们建议您先完成“TensorFlow实践专业”。为了深入理解神经网络的工作原理，我们还推荐您参加“深度学习专业”。 课程大纲包括： - TensorFlow Extended - 与TensorFlow Hub共享预训练模型 - TensorBoard：模型训练工具 - 联邦学习
Name:TensorFlow Extended
Description:
Name:Sharing pre-trained models with TensorFlow Hub
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Name:Tensorboard: tools for model training
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Name:Federated Learning
Description:
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.
