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第 1 门课程(共 4 门)

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中级

Basic understanding of JavaScript

完成时间大约为13 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

  • Train and run inference in a browser

  • Handle data in a browser

  • Build an object classification and recognition model using a webcam

您将获得的技能

Convolutional Neural NetworkMachine LearningTensorflowObject DetectionTensorFlow.js

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 1 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 JavaScript

完成时间大约为13 小时

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

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 5 小时

Introduction to TensorFlow.js

完成时间为 5 小时
11 个视频 (总计 30 分钟), 7 个阅读材料, 3 个测验
11 个视频
Course Introduction, A Conversation with Andrew Ng1分钟
A Few Words From Laurence2分钟
Building the Model3分钟
Training the Model3分钟
First Example In Code4分钟
The Iris Dataset1分钟
Reading the Data4分钟
One-hot Encoding1分钟
Designing the NN2分钟
Iris Classifier In Code6分钟
7 个阅读材料
Getting Your System Ready10分钟
Downloading the Coding Examples and Exercises10分钟
Your First Model10分钟
Iris Dataset Documentation10分钟
Using the Web Server10分钟
Iris Classifier10分钟
Week 1 Wrap up10分钟
2 个练习
Quiz 1
One-Hot Encoding
2

2

完成时间为 4 小时

Image Classification In the Browser

完成时间为 4 小时
8 个视频 (总计 27 分钟), 5 个阅读材料, 2 个测验
8 个视频
Creating a Convolutional Net with JavaScript4分钟
Visualizing the Training Process2分钟
What Is a Sprite Sheet?1分钟
Using the Sprite Sheet2分钟
Using tf.tidy() to Save Memory1分钟
A Few Words From Laurence24
MNIST Classifier In Code13分钟
5 个阅读材料
tjs-vis Documentation10分钟
MNIST Sprite Sheet10分钟
MNIST Classifier10分钟
Week 2 Wrap up10分钟
Exercise Description10分钟
1 个练习
Week 2 Quiz
3

3

完成时间为 5 小时

Converting Models to JSON Format

完成时间为 5 小时
12 个视频 (总计 28 分钟), 7 个阅读材料, 2 个测验
12 个视频
A Few Words From Laurence1分钟
Pre-trained TensorFlow.js Models49
Toxicity Classifier3分钟
Toxicity Classifier In Code3分钟
MobileNet49
Using MobileNet1分钟
Training Results1分钟
MobileNet Example In Code3分钟
Converting Models to JavaScript4分钟
Converting Models to JavaScript In Code2分钟
Linear Example In Code1分钟
7 个阅读材料
Important Links10分钟
Toxicity Classifier10分钟
Classes Supported by MobileNet10分钟
Image Classification Using MobileNet10分钟
Linear Model10分钟
Week 3 Wrap up10分钟
Optional - Install Wget (Only If Needed)10分钟
1 个练习
Week 3 Quiz
4

4

完成时间为 4 小时

Transfer Learning with Pre-Trained Models

完成时间为 4 小时
11 个视频 (总计 26 分钟), 3 个阅读材料, 2 个测验
11 个视频
A Few Words From Laurence53
Building a Simple Web Page2分钟
Retraining the MobileNet Model1分钟
The Training Function2分钟
Capturing the Data3分钟
The Dataset Class2分钟
Training the Network with the Captured Data1分钟
Performing Inference4分钟
Rock Paper Scissors In Code4分钟
A Conversation with Andrew Ng1分钟
3 个阅读材料
Rock Paper Scissors10分钟
Exercise Description10分钟
Wrap up10分钟
1 个练习
Week 4 Quiz

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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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