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.
提供方
TensorFlow: Data and Deployment 專項課程deeplearning.ai課程信息
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完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 4 門課程（共 4 門）
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根據您的日程表重置截止日期。
中級
We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.
完成時間大約為12 小時
英語（English）
字幕：英語（English）
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 ServingMachine Learningfederated learningTensorFlow HubTensorBoard
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 4 門課程（共 4 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
中級
We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.
完成時間大約為12 小時
英語（English）
字幕：英語（English）
提供方
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 1 小時
TensorFlow Extended
完成時間為 1 小時
12 個視頻 （總計 21 分鐘）, 5 個閱讀材料, 1 個測驗
12 個視頻
Introduction24
Serving3分鐘
Installing TF Serving1分鐘
TensorFlow Serving summary30
Setup for serving2分鐘
Serving1分鐘
Predictions41
Passing data to serving1分鐘
Getting the predictions back1分鐘
Running the colab2分鐘
Complex model2分鐘
5 個閱讀材料
Downloading the Coding Examples and Exercises10分鐘
Installation link10分鐘
TF server running in colab10分鐘
Serving with Fashion MNIST10分鐘
Ungraded Exercise - Serving with MNIST10分鐘
1 個練習
Week 1 Quiz
完成時間為 5 小時
Sharing pre-trained models with TensorFlow Hub
完成時間為 5 小時
11 個視頻 （總計 20 分鐘）, 7 個閱讀材料, 2 個測驗
11 個視頻
Introduction to TF Hub2分鐘
Transfer learning1分鐘
Inference1分鐘
Module storage2分鐘
Text based models1分鐘
Word embeddings1分鐘
Experimenting with embeddings1分鐘
Colab1分鐘
Classify cats and dogs1分鐘
Transfer learning1分鐘
7 個閱讀材料
Tensorflow Hub link10分鐘
Link to saved models10分鐘
Colab10分鐘
Pre-trained Word Embeddings10分鐘
Text Classification Colab10分鐘
MobileNet model details10分鐘
Colab10分鐘
1 個練習
Week 2 Quiz
完成時間為 5 小時
Tensorboard: tools for model training
完成時間為 5 小時
10 個視頻 （總計 16 分鐘）, 2 個閱讀材料, 2 個測驗
10 個視頻
Tensorboard scalars1分鐘
Callbacks42
Histograms59
Publishing model details1分鐘
Local tensorboard2分鐘
Looking at graphics in a dataset2分鐘
More than one image56
Confusion matrix2分鐘
Multiple callbacks1分鐘
2 個閱讀材料
tensorboard.dev10分鐘
Colab10分鐘
1 個練習
Week 3 Quiz4分鐘
完成時間為 1 小時
Federated Learning
完成時間為 1 小時
9 個視頻 （總計 22 分鐘）, 1 個閱讀材料, 1 個測驗
9 個視頻
Training on mobile devices2分鐘
Data at the edge2分鐘
How it works2分鐘
Maintaining user privacy3分鐘
Masking2分鐘
APIs for Federated Learning2分鐘
Example of federated learning2分鐘
Outro59
1 個閱讀材料
Colab10分鐘
1 個練習
Week 4 Quiz30分鐘
審閱
來自ADVANCED DEPLOYMENT SCENARIOS WITH TENSORFLOW的熱門評論
由 DR 提供2020年6月6日
Enjoyed this specialization as much as I did the Tensorflow in practice. Thank you Laurence Moroney and Andrew Ng for getting these cool topics to all of us, so we can contribute back to community.
由 FL 提供2020年3月19日
The first week is very useful. Tensorhub seems not helpful in a private company setting.\n\nI wish they talked about tf estimators which is also a common way to deploy models in SageMaker.
由 SM 提供2020年4月10日
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
由 DB 提供2020年5月17日
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.
In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more.
Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever.
This Specialization builds upon skills learned in the TensorFlow in Practice Specialization. We recommend learners complete that Specialization prior to enrolling in TensorFlow: Data and Deployment.
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