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.
本课程是 TensorFlow: Data and Deployment 专项课程 专项课程的一部分
Browser-based Models with TensorFlow.js
提供方
课程信息
83,345 次近期查看
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）
提供方
deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
教学大纲 - 您将从这门课程中学到什么
完成时间为 5 小时
Introduction to TensorFlow.js
Welcome to Browser-based Models with TensorFlow.js, the first course of the TensorFlow for Data and Deployment Specialization. In this first course, we’re going to look at how to train machine learning models in the browser and how to use them to perform inference using JavaScript. This will allow you to use machine learning directly in the browser as well as on backend servers like Node.js. In the first week of the course, we are going to build some basic models using JavaScript and we'll execute them in simple web pages.
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
完成时间为 4 小时
Image Classification In the Browser
This week we'll look at Computer Vision problems, including some of the unique considerations when using JavaScript, such as handling thousands of images for training. By the end of this module you will know how to build a site that lets you draw in the browser and recognizes your handwritten digits!
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
完成时间为 5 小时
Converting Models to JSON Format
This week we'll see how to take models that have been created with TensorFlow in Python and convert them to JSON format so that they can run in the browser using Javascript. We will start by looking at two models that have already been pre-converted. One of them is going to be a toxicity classifier, which uses NLP to determine if a phrase is toxic in a number of categories; the other one is Mobilenet which can be used to detect content in images. By the end of this module, you will train a model in Python yourself and convert it to JSON format using the tensorflow.js converter.
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
完成时间为 4 小时
Transfer Learning with Pre-Trained Models
One final work type that you'll need when creating Machine Learned applications in the browser is to understand how transfer learning works. This week you'll build a complete web site that uses TensorFlow.js, capturing data from the web cam, and re-training mobilenet to recognize Rock, Paper and Scissors gestures.
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
审阅
4.7
来自BROWSER-BASED MODELS WITH TENSORFLOW.JS的热门评论
由 AZ 提供Mar 28, 2020
No doubt, the team of Deeplearning.AI is building best learning resources. We would love to get more and more resources for easy way learning from DeepLearning.AI Team.\n\nThanks to all.
由 ZB 提供Dec 19, 2019
Awesome - elegant in its complex simplicity. Clear explanations, logical curriculum structure, nice and knowledgeable code examples. A must-complete course indeed!
由 SS 提供Jan 1, 2020
Thanks to Laurence and Andrew for designing such a great course. I learnt a lot from this course and looking forward to learn more from both of you.
由 SZ 提供Feb 23, 2020
Awesome course! This is one of the most practical courses I have taken, and I am looking forward to the next courses in the series. Thanks! - Steve
由 JC 提供Mar 2, 2020
I really enjoy working on the programming assignments of this course especially the Week 4 one which is fun and have a lot to learn!
由 VV 提供Dec 17, 2019
Great course! A bit of a learning curve on javascript, but really worth it! The exercise in Week 4 was phenomenal! Thanks.
由 PM 提供Dec 17, 2019
Last exercise was really difficult to pass! Probably due to overfitting and not generalising to the the test set.
由 PG 提供Dec 16, 2019
Great videos and nice coding challenges. Would like some more support on various error messages though
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.
常见问题
我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
还有其他问题吗？请访问 学生帮助中心。