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

Laurence Moroney

提供方

TensorFlow: Data and Deployment 專項課程deeplearning.ai

課程信息

23,723 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

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）

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）

TensorFlow: Data and Deployment 專項課程

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）

講師

授課教師評分

4.82/5 （17 個評分）

Laurence Moroney

Instructor

Lead AI Advocate, Google

267,815 個學生

13 門課程

提供方

deeplearning.ai

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

教學大綱 - 您將從這門課程中學到什麼

週

週 1

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

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 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

週

週 2

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, 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 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

Introduction, A conversation with Andrew Ng2分鐘

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

週

週 4

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

Introduction, A conversation with Andrew Ng2分鐘

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

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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Advanced Deployment Scenarios with TensorFlow