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.
このコースはTensorFlow: Data and Deployment の専門講座に属します。
Browser-based Models with TensorFlow.js
提供：
このコースについて
84,995 最近の表示
学習内容
Train and run inference in a browser
Handle data in a browser
Build an object classification and recognition model using a webcam
習得するスキル
Convolutional Neural NetworkMachine LearningTensorflowObject DetectionTensorFlow.js
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の1コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
Basic understanding of JavaScript
約12時間で修了
推奨：4 weeks of study, 4-5 hours/week
英語
字幕：英語
提供：
deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
シラバス - 本コースの学習内容
5時間で修了
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.
5時間で修了
11件のビデオ （合計30分）, 7 readings, 3 quizzes
11件のビデオ
Course Introduction, A Conversation with Andrew Ng1 分
A Few Words From Laurence2 分
Building the Model3 分
Training the Model3 分
First Example In Code4 分
The Iris Dataset1 分
Reading the Data4 分
One-hot Encoding1 分
Designing the NN2 分
Iris Classifier In Code6 分
7件の学習用教材
Getting Your System Ready10 分
Downloading the Coding Examples and Exercises10 分
Your First Model10 分
Iris Dataset Documentation10 分
Using the Web Server10 分
Iris Classifier10 分
Week 1 Wrap up10 分
2の練習問題
Quiz 1
One-Hot Encoding
4時間で修了
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!
4時間で修了
8件のビデオ （合計27分）, 5 readings, 2 quizzes
8件のビデオ
Creating a Convolutional Net with JavaScript4 分
Visualizing the Training Process2 分
What Is a Sprite Sheet?1 分
Using the Sprite Sheet2 分
Using tf.tidy() to Save Memory1 分
A Few Words From Laurence24
MNIST Classifier In Code13 分
5件の学習用教材
tjs-vis Documentation10 分
MNIST Sprite Sheet10 分
MNIST Classifier10 分
Week 2 Wrap up10 分
Exercise Description10 分
1の練習問題
Week 2 Quiz
5時間で修了
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.
5時間で修了
12件のビデオ （合計28分）, 7 readings, 2 quizzes
12件のビデオ
A Few Words From Laurence1 分
Pre-trained TensorFlow.js Models49
Toxicity Classifier3 分
Toxicity Classifier In Code3 分
MobileNet49
Using MobileNet1 分
Training Results1 分
MobileNet Example In Code3 分
Converting Models to JavaScript4 分
Converting Models to JavaScript In Code2 分
Linear Example In Code1 分
7件の学習用教材
Important Links10 分
Toxicity Classifier10 分
Classes Supported by MobileNet10 分
Image Classification Using MobileNet10 分
Linear Model10 分
Week 3 Wrap up10 分
Optional - Install Wget (Only If Needed)10 分
1の練習問題
Week 3 Quiz
4時間で修了
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.
4時間で修了
11件のビデオ （合計26分）, 3 readings, 2 quizzes
11件のビデオ
A Few Words From Laurence53
Building a Simple Web Page2 分
Retraining the MobileNet Model1 分
The Training Function2 分
Capturing the Data3 分
The Dataset Class2 分
Training the Network with the Captured Data1 分
Performing Inference4 分
Rock Paper Scissors In Code4 分
A Conversation with Andrew Ng1 分
3件の学習用教材
Rock Paper Scissors10 分
Exercise Description10 分
Wrap up10 分
1の練習問題
Week 4 Quiz
レビュー
4.7
BROWSER-BASED MODELS WITH TENSORFLOW.JS からの人気レビュー
AZにより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.
ZBによりDec 19, 2019
Awesome - elegant in its complex simplicity. Clear explanations, logical curriculum structure, nice and knowledgeable code examples. A must-complete course indeed!
SSにより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.
SZにより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
JCにより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!
VVにより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.
PMによりDec 17, 2019
Last exercise was really difficult to pass! Probably due to overfitting and not generalising to the the test set.
PGによりDec 16, 2019
Great videos and nice coding challenges. Would like some more support on various error messages though
RSによりFeb 19, 2020
Great resource to build practical, real-world examples...easily explained with lots of sample code.
ECによりJan 4, 2020
Excellent! well presented and guided through. A good starting point to begin playing with the tech!
MBによりDec 27, 2019
Great introduction on browser-based models. Well presented with great code examples and exercises.
OHによりFeb 6, 2020
This was an awesome course with everything well explained and with some really cool projects.
TensorFlow: Data and Deployment専門講座について
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.
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