課程信息

51,878 次近期查看

100% 在線

立即開始,按照自己的計劃學習。

第 2 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

Basic understanding of Kotlin and/or Swift

完成時間大約為11 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

  • Prepare models for battery-operated devices

  • Execute models on Android and iOS platforms

  • Deploy models on embedded systems like Raspberry Pi and microcontrollers

您將獲得的技能

TensorFlow LiteMathematical OptimizationMachine LearningTensorflowObject Detection

100% 在線

立即開始,按照自己的計劃學習。

第 2 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

Basic understanding of Kotlin and/or Swift

完成時間大約為11 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 6 小時

Device-based models with TensorFlow Lite

完成時間為 6 小時
14 個視頻 (總計 40 分鐘), 6 個閱讀材料, 2 個測驗
14 個視頻
A few words from Laurence55
Features and components of mobile AI2分鐘
Architecture and performance3分鐘
Optimization Techniques2分鐘
Saving, converting, and optimizing a model3分鐘
Examples2分鐘
Quantization3分鐘
TF-Select1分鐘
Paths in Optimization1分鐘
Running the models1分鐘
Transfer learning3分鐘
Converting a model to TFLite1分鐘
Transfer learning with TFLite5分鐘
6 個閱讀材料
Prerequisites10分鐘
Downloading the Coding Examples and Exercises10分鐘
GPU delegates10分鐘
Learn about supported ops and TF-Select10分鐘
Week 1 Wrap up10分鐘
Exercise Description10分鐘
1 個練習
Week 1 Quiz
2

2

完成時間為 1 小時

Running a TF model in an Android App

完成時間為 1 小時
15 個視頻 (總計 36 分鐘), 3 個閱讀材料, 1 個測驗
15 個視頻
Installation and resources2分鐘
Architecture of a model1分鐘
Initializing the Interpreter2分鐘
Preparing the Input1分鐘
Inference and results1分鐘
Code walkthrough3分鐘
Run the App2分鐘
Classifying camera images55
Initialize and prepare input3分鐘
Demo of camera image classifier4分鐘
Initialize model and prepare inputs1分鐘
Inference and results3分鐘
Demo of the object detection App1分鐘
Code for the inference and results2分鐘
3 個閱讀材料
Android fundamentals and installation10分鐘
Week 2 Wrap up10分鐘
Description10分鐘
1 個練習
Week 2 Quiz
3

3

完成時間為 2 小時

Building the TensorFLow model on IOS

完成時間為 2 小時
22 個視頻 (總計 45 分鐘), 8 個閱讀材料, 1 個測驗
22 個視頻
A few words from Laurence1分鐘
What is Swift?45
TerserflowLiteSwift1分鐘
Cats vs Dogs App1分鐘
Taking the initial steps3分鐘
Scaling the image2分鐘
More steps in the process3分鐘
Looking at the App in Xcode5分鐘
What have we done so far and how do we continue?41
Using the App50
App architecture1分鐘
Model details1分鐘
Initial steps4分鐘
Final steps1分鐘
Looking at the code for the image classification App4分鐘
Object classification intro30
TFL detect App53
App architecture55
Initial steps58
Final steps3分鐘
Looking at the code for the object detection model3分鐘
8 個閱讀材料
Important links10分鐘
Apple’s developer's site 10分鐘
Apple's API10分鐘
More details10分鐘
Camera related functionalities10分鐘
The Coco dataset10分鐘
Week 3 Wrap up10分鐘
Description10分鐘
1 個練習
Week 3 Quiz
4

4

完成時間為 2 小時

TensorFlow Lite on devices

完成時間為 2 小時
13 個視頻 (總計 29 分鐘), 7 個閱讀材料, 1 個測驗
13 個視頻
A few words from Laurence3分鐘
Devices3分鐘
Starting to work on a Raspberry Pi1分鐘
How do we start?2分鐘
Image classification1分鐘
The 4 step process2分鐘
Object detection1分鐘
Back to the 4 step process4分鐘
Raspberry Pi demo2分鐘
Microcontrollers2分鐘
Closing words by Laurence28
A conversation with Andrew Ng1分鐘
7 個閱讀材料
Edge TPU models10分鐘
Options to choose from10分鐘
Pre optimized mobileNet10分鐘
Object detection model trained on the coco10分鐘
Suggested links10分鐘
Description10分鐘
Wrap up10分鐘
1 個練習
Week 4 Quiz

審閱

來自DEVICE-BASED MODELS WITH TENSORFLOW LITE的熱門評論
查看所有評論

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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