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.
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課程信息
We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.
您將學到的內容有
Use TensorFlow Serving to do inference over the web
Navigate TensorFlow Hub, a repository of models that you can use for transfer learning
Evaluate how your models work and share model metadata using TensorBoard
Explore federated learning and how to retrain deployed models while maintaining data privacy
您將獲得的技能
- TensorFlow Serving
- Machine Learning
- federated learning
- TensorFlow Hub
- TensorBoard
We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.
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deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
授課大綱 - 您將從這門課程中學到什麼
TensorFlow Extended
Sharing pre-trained models with TensorFlow Hub
Tensorboard: tools for model training
Federated Learning
審閱
- 5 stars81.11%
- 4 stars14.45%
- 3 stars3.03%
- 2 stars0.93%
- 1 star0.46%
來自ADVANCED DEPLOYMENT SCENARIOS WITH TENSORFLOW的熱門評論
This topic seems to be very important but the content is not as good as rest of the weeks content, more explanations and more simple examples could have helped
Awesome course for the using and application of Machine learning
The fact that there were still some problems in the Course regarding technical or exercise based, it shows that this material is relatively new in the domain.
Great work and I highly recommend this course/specialization! Good job of inserting needed edits to update what's happening in real time.
關於 TensorFlow: Data and Deployment 專項課程
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models.

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