本課程是 TensorFlow: Data and Deployment 專項課程專項課程的一部分

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51 條評論

Laurence Moroney

16,334 人已註冊

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TensorFlow: Data and Deployment 專項課程deeplearning.ai

課程信息

153,948 次近期查看

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.

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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）

TensorFlow: Data and Deployment 專項課程

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18 小時

英語（English）

字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）

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

可靈活調整截止日期

根據您的日程表重置截止日期。

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）

TensorFlow: Data and Deployment 專項課程

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18 小時

英語（English）

授課教師

授課教師評分

4.89/5 （39 個評分）

Laurence Moroney

Instructor

Lead AI Advocate, Google

364,788 個學生

15 門課程

提供方

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

內容評分96%（1,045 個評分）

週

週 1

完成時間為 3 小時

TensorFlow Extended

完成時間為 3 小時

12 個視頻（總計 21 分鐘）, 5 個閱讀材料, 3 個測驗

12 個視頻

Introduction, A conversation with Andrew Ng2分鐘

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 Ungraded Labs and Programming Assignments10分鐘

Installation link10分鐘

TF server running in colab10分鐘

Serving with Fashion MNIST10分鐘

Ungraded Assignment - Serving with MNIST10分鐘

1 個練習

Week 1 Quiz

週

週 2

完成時間為 6 小時

Sharing pre-trained models with TensorFlow Hub

完成時間為 6 小時

11 個視頻（總計 20 分鐘）, 7 個閱讀材料, 3 個測驗

11 個視頻

Introduction, A conversation with Andrew Ng2分鐘

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

週

週 3

完成時間為 6 小時

Tensorboard: tools for model training

完成時間為 6 小時

10 個視頻（總計 16 分鐘）, 2 個閱讀材料, 3 個測驗

週

週 4

完成時間為 2 小時

Federated Learning

完成時間為 2 小時

9 個視頻（總計 22 分鐘）, 3 個閱讀材料, 2 個測驗

審閱

4.7

51 條評論

5 stars
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4 stars
14.45%
3 stars
3.03%
2 stars
0.93%
1 star
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來自ADVANCED DEPLOYMENT SCENARIOS WITH TENSORFLOW的熱門評論

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 AS 提供2020年5月15日

Awesome course for the using and application of Machine learning

由 AS 提供2021年1月21日

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.

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.

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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. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.

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Advanced Deployment Scenarios with TensorFlow