課程信息

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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 3 門課程(共 4 門)

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 a basic familiarity with building models in TensorFlow

完成時間大約為13 小時

建議:4 weeks of study, 4-5 hours/week...

英語(English)

字幕:英語(English)

您將學到的內容有

  • Leverage built-in datasets with just a few lines of code

  • Use APIs to control how you split your data

  • Process all types of unstructured data

您將獲得的技能

TensorflowMachine Learning

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 3 門課程(共 4 門)

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 a basic familiarity with building models in TensorFlow

完成時間大約為13 小時

建議:4 weeks of study, 4-5 hours/week...

英語(English)

字幕:英語(English)

提供方

deeplearning.ai 徽標

deeplearning.ai

教學大綱 - 您將從這門課程中學到什麼

1

1

完成時間為 5 小時

Data Pipelines with TensorFlow Data Services

完成時間為 5 小時
14 個視頻 (總計 27 分鐘), 2 個閱讀材料, 2 個測驗
14 個視頻
Introduction1分鐘
Popular datasets2分鐘
Data pipelines58
Extract, transform, load3分鐘
Versioning datasets2分鐘
Looking at the notebook1分鐘
Introduction43
Legacy API and Subsplits5分鐘
Splits API (S3)2分鐘
Introduction22
Legacy API in code1分鐘
Splits API (S3) in code1分鐘
Week 1 wrap up43
2 個閱讀材料
Downloading the Coding Examples and Exercises10分鐘
Try out the notebook yourself10分鐘
1 個練習
Week 1 Quiz
2

2

完成時間為 6 小時

Exporting your data into the training pipeline

完成時間為 6 小時
21 個視頻 (總計 44 分鐘), 5 個閱讀材料, 2 個測驗
21 個視頻
Introduction22
Input data1分鐘
Basic mechanics2分鐘
Numeric and bucketized columns2分鐘
Vocabulary and hashed columns, feature crossing2分鐘
Embedding columns2分鐘
Introduction24
Notebook walkthrough4分鐘
Introduction19
Numpy, Pandas and Images2分鐘
CSV3分鐘
Text and TFRecord1分鐘
Generators1分鐘
Introduction17
Notebook walkthrough4分鐘
Introduction1分鐘
Numpy and Pandas2分鐘
Images1分鐘
CSV4分鐘
Text2分鐘
5 個閱讀材料
Link to the notebook10分鐘
Link to the CNN course10分鐘
Link to the notebook10分鐘
CSV: colab10分鐘
Link to the tokenization10分鐘
1 個練習
Week 2 Quiz
3

3

完成時間為 4 小時

Performance

完成時間為 4 小時
11 個視頻 (總計 20 分鐘)
11 個視頻
Introduction36
ETL2分鐘
What happens when you train a model2分鐘
Introduction25
Caching58
Parallelism APIs2分鐘
Autotuning2分鐘
Parallelizing data extraction2分鐘
Best practices for code improvements3分鐘
A few words by Laurence34
1 個練習
Week 3 Quiz
4

4

完成時間為 5 小時

Publishing your datasets

完成時間為 5 小時
11 個視頻 (總計 24 分鐘), 2 個閱讀材料, 2 個測驗
11 個視頻
Introduction44
How to start using a dataset2分鐘
Implementation4分鐘
File access and possible problems in data3分鐘
Publishing the dataset3分鐘
Introduction18
Going through the colab (1)2分鐘
Going through the colab (2)2分鐘
Closing words14
A conversation with Andrew Ng1分鐘
2 個閱讀材料
URLs10分鐘
Link to the colab10分鐘
1 個練習
Week 4 Quiz

審閱

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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. 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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