本课程是 TensorFlow: Data and Deployment 专项课程专项课程的一部分

Laurence Moroney

提供方

TensorFlow: Data and Deployment 专项课程deeplearning.ai

课程信息

23,504 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.

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

第 4 门课程（共 4 门）

TensorFlow: Data and Deployment 专项课程

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.

完成时间大约为12 小时

英语（English）

字幕：英语（English）

您将学到的内容有

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

您将获得的技能

TensorFlow ServingMachine Learningfederated learningTensorFlow HubTensorBoard

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

第 4 门课程（共 4 门）

TensorFlow: Data and Deployment 专项课程

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have basic familiarity with building models in TensorFlow.

完成时间大约为12 小时

英语（English）

字幕：英语（English）

讲师

授课教师评分

4.82/5 （17 个评分）

Laurence Moroney

Instructor

Lead AI Advocate, Google

268,111 个学生

12 门课程

提供方

deeplearning.ai

Founded by Andrew Ng, DeepLearning.AI is an education technology company that develops a global community of AI talent.

教学大纲 - 您将从这门课程中学到什么

周

周 1

完成时间为 1 小时

TensorFlow Extended

完成时间为 1 小时

12 个视频（总计 21 分钟）, 5 个阅读材料, 1 个测验

12 个视频

Introduction, A conversation with Andrew Ng2分钟

Introduction24

Serving3分钟

Installing TF Serving1分钟

TensorFlow Serving summary30

Setup for serving2分钟

Serving1分钟

Predictions41

Passing data to serving1分钟

Getting the predictions back1分钟

Running the colab2分钟

Complex model2分钟

5 个阅读材料

Downloading the Coding Examples and Exercises10分钟

Installation link10分钟

TF server running in colab10分钟

Serving with Fashion MNIST10分钟

Ungraded Exercise - Serving with MNIST10分钟

1 个练习

Week 1 Quiz

周

周 2

完成时间为 5 小时

Sharing pre-trained models with TensorFlow Hub

完成时间为 5 小时

11 个视频（总计 20 分钟）, 7 个阅读材料, 2 个测验

11 个视频

Introduction, A conversation with Andrew Ng2分钟

Introduction to TF Hub2分钟

Transfer learning1分钟

Inference1分钟

Module storage2分钟

Text based models1分钟

Word embeddings1分钟

Experimenting with embeddings1分钟

Colab1分钟

Classify cats and dogs1分钟

Transfer learning1分钟

7 个阅读材料

Tensorflow Hub link10分钟

Link to saved models10分钟

Colab10分钟

Pre-trained Word Embeddings10分钟

Text Classification Colab10分钟

MobileNet model details10分钟

Colab10分钟

1 个练习

Week 2 Quiz

周

周 3

完成时间为 5 小时

Tensorboard: tools for model training

完成时间为 5 小时

10 个视频（总计 16 分钟）, 2 个阅读材料, 2 个测验

10 个视频

Introduction, A conversation with Andrew Ng2分钟

Tensorboard scalars1分钟

Callbacks42

Histograms59

Publishing model details1分钟

Local tensorboard2分钟

Looking at graphics in a dataset2分钟

More than one image56

Confusion matrix2分钟

Multiple callbacks1分钟

2 个阅读材料

tensorboard.dev10分钟

Colab10分钟

1 个练习

Week 3 Quiz4分钟

周

周 4

完成时间为 1 小时

Federated Learning

完成时间为 1 小时

9 个视频（总计 22 分钟）, 1 个阅读材料, 1 个测验

9 个视频

Introduction, A conversation with Andrew Ng2分钟

Training on mobile devices2分钟

Data at the edge2分钟

How it works2分钟

Maintaining user privacy3分钟

Masking2分钟

APIs for Federated Learning2分钟

Example of federated learning2分钟

Outro59

1 个阅读材料

Colab10分钟

1 个练习

Week 4 Quiz30分钟

关于 TensorFlow: Data and Deployment 专项课程

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.

常见问题

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课程可能不提供旁听选项。您可以尝试免费试用，也可以申请助学金。
课程可能会改为提供'完整课程，没有证书'。通过此选项，您可以查看所有课程材料、提交所要求的作业，以及获得最终成绩。这也意味着您将无法购买证书体验。
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您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
Is financial aid available?
是的，Coursera 可以为无法承担费用的学生提供助学金。通过点击左侧“注册”按钮下的“助学金”链接可以申请助学金。您可以根据屏幕提示完成申请，申请获批后会收到通知。您需要针对专项课程中的每一门课程完成上述步骤，包括毕业项目。了解更多。

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Advanced Deployment Scenarios with TensorFlow