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

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Laurence Moroney

提供方

TensorFlow: Data and Deployment 专项课程deeplearning.ai

课程信息

14,587 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13 小时

英语（English）

字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）

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13 小时

英语（English）

讲师

授课教师评分

4.8/5 （20 个评分）

Laurence Moroney

Instructor

Lead AI Advocate, Google

287,152 个学生

14 门课程

提供方

deeplearning.ai

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

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）, 2 个阅读材料, 1 个测验

审阅

4.7

35 条评论

5 stars
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4 stars
16.39%
3 stars
4.91%
2 stars
1.63%
1 star
0.81%

来自ADVANCED DEPLOYMENT SCENARIOS WITH TENSORFLOW的热门评论

由 DR 提供Jun 6, 2020

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Mar 19, 2020

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Apr 10, 2020

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Jan 21, 2021

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.

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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. 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