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立即开始,按照自己的计划学习。

第 2 门课程(共 4 门)

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中级

Basic understanding of Kotlin and/or Swift

完成时间大约为10 小时

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

英语(English)

字幕:英语(English)

您将学到的内容有

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    Prepare models for battery-operated devices

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    Execute models on Android and iOS platforms

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    Deploy models on embedded systems like Raspberry Pi and microcontrollers

您将获得的技能

TensorFlow LiteMathematical OptimizationMachine LearningTensorflowObject Detection

100% 在线

立即开始,按照自己的计划学习。

第 2 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

Basic understanding of Kotlin and/or Swift

完成时间大约为10 小时

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

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 6 小时

Device-based models with TensorFlow Lite

14 个视频 (总计 40 分钟), 6 个阅读材料, 2 个测验
14 个视频
A few words from Laurence55
Features and components of mobile AI2分钟
Architecture and performance3分钟
Optimization Techniques2分钟
Saving, converting, and optimizing a model3分钟
Examples2分钟
Quantization3分钟
TF-Select1分钟
Paths in Optimization1分钟
Running the models1分钟
Transfer learning3分钟
Converting a model to TFLite1分钟
Transfer learning with TFLite5分钟
6 个阅读材料
Prerequisites10分钟
Downloading the Coding Examples and Exercises10分钟
GPU delegates10分钟
Learn about supported ops and TF-Select10分钟
Week 1 Wrap up10分钟
Exercise Description10分钟
1 个练习
Week 1 Quiz
2
完成时间为 1 小时

Running a TF model in an Android App

15 个视频 (总计 36 分钟), 3 个阅读材料, 1 个测验
15 个视频
Installation and resources2分钟
Architecture of a model1分钟
Initializing the Interpreter2分钟
Preparing the Input1分钟
Inference and results1分钟
Code walkthrough3分钟
Run the App2分钟
Classifying camera images55
Initialize and prepare input3分钟
Demo of camera image classifier4分钟
Initialize model and prepare inputs1分钟
Inference and results3分钟
Demo of the object detection App1分钟
Code for the inference and results2分钟
3 个阅读材料
Android fundamentals and installation10分钟
Week 2 Wrap up10分钟
Description10分钟
1 个练习
Week 2 Quiz
3
完成时间为 2 小时

Building the TensorFLow model on IOS

22 个视频 (总计 45 分钟), 8 个阅读材料, 1 个测验
22 个视频
A few words from Laurence1分钟
What is Swift?45
TerserflowLiteSwift1分钟
Cats vs Dogs App1分钟
Taking the initial steps3分钟
Scaling the image2分钟
More steps in the process3分钟
Looking at the App in Xcode5分钟
What have we done so far and how do we continue?41
Using the App50
App architecture1分钟
Model details1分钟
Initial steps4分钟
Final steps1分钟
Looking at the code for the image classification App4分钟
Object classification intro30
TFL detect App53
App architecture55
Initial steps58
Final steps3分钟
Looking at the code for the object detection model3分钟
8 个阅读材料
Important links10分钟
Apple’s developer's site 10分钟
Apple's API10分钟
More details10分钟
Camera related functionalities10分钟
The Coco dataset10分钟
Week 3 Wrap up10分钟
Description10分钟
1 个练习
Week 3 Quiz
4
完成时间为 2 小时

TensorFlow Lite on devices

13 个视频 (总计 29 分钟), 7 个阅读材料, 1 个测验
13 个视频
A few words from Laurence3分钟
Devices3分钟
Starting to work on a Raspberry Pi1分钟
How do we start?2分钟
Image classification1分钟
The 4 step process2分钟
Object detection1分钟
Back to the 4 step process4分钟
Raspberry Pi demo2分钟
Microcontrollers2分钟
Closing words by Laurence28
A conversation with Andrew Ng1分钟
7 个阅读材料
Edge TPU models10分钟
Options to choose from10分钟
Pre optimized mobileNet10分钟
Object detection model trained on the coco10分钟
Suggested links10分钟
Description10分钟
Wrap up10分钟
1 个练习
Week 4 Quiz
4.5
6 条评论

来自Device-based Models with TensorFlow Lite的热门评论

创建者 MRJan 5th 2020

A great course to learn how to implement any Deep Learning models on edge devices.

讲师

Image of instructor, Laurence Moroney

Laurence Moroney

AI Advocate
Google Brain

关于 deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

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 model. 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, use APIs to control how data splitting, and process all types of unstructured data. 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 AI. 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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