课程信息

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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)
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)
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

提供方

deeplearning.ai 徽标

deeplearning.ai

教学大纲 - 您将从这门课程中学到什么

1

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
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
2

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 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
3

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
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
4

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
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

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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. 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....
TensorFlow: Data and Deployment

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