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.
이 강좌는 TensorFlow: Data and Deployment 전문 분야의 일부입니다.
Browser-based Models with TensorFlow.js
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About this Course
최근 조회 80,194회
배울 내용
Train and run inference in a browser
Handle data in a browser
Build an object classification and recognition model using a webcam
귀하가 습득할 기술
Convolutional Neural NetworkMachine LearningTensorflowObject DetectionTensorFlow.js
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 4개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
Basic understanding of JavaScript
완료하는 데 약 13시간 필요
권장: 4 weeks of study, 4-5 hours/week
영어
자막: 영어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 5시간 필요
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.
완료하는 데 5시간 필요
11개 동영상 (총 30분), 7 readings, 3 quizzes
11개의 동영상
Course Introduction, A Conversation with Andrew Ng1m
A Few Words From Laurence2m
Building the Model3m
Training the Model3m
First Example In Code4m
The Iris Dataset1m
Reading the Data4m
One-hot Encoding1m
Designing the NN2m
Iris Classifier In Code6m
7개의 읽기 자료
Getting Your System Ready10m
Downloading the Coding Examples and Exercises10m
Your First Model10m
Iris Dataset Documentation10m
Using the Web Server10m
Iris Classifier10m
Week 1 Wrap up10m
2개 연습문제
Quiz 1
One-Hot Encoding
완료하는 데 4시간 필요
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!
완료하는 데 4시간 필요
8개 동영상 (총 27분), 5 readings, 2 quizzes
8개의 동영상
Creating a Convolutional Net with JavaScript4m
Visualizing the Training Process2m
What Is a Sprite Sheet?1m
Using the Sprite Sheet2m
Using tf.tidy() to Save Memory1m
A Few Words From Laurence24
MNIST Classifier In Code13m
5개의 읽기 자료
tjs-vis Documentation10m
MNIST Sprite Sheet10m
MNIST Classifier10m
Week 2 Wrap up10m
Exercise Description10m
1개 연습문제
Week 2 Quiz
완료하는 데 5시간 필요
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.
완료하는 데 5시간 필요
12개 동영상 (총 28분), 7 readings, 2 quizzes
12개의 동영상
A Few Words From Laurence1m
Pre-trained TensorFlow.js Models49
Toxicity Classifier3m
Toxicity Classifier In Code3m
MobileNet49
Using MobileNet1m
Training Results1m
MobileNet Example In Code3m
Converting Models to JavaScript4m
Converting Models to JavaScript In Code2m
Linear Example In Code1m
7개의 읽기 자료
Important Links10m
Toxicity Classifier10m
Classes Supported by MobileNet10m
Image Classification Using MobileNet10m
Linear Model10m
Week 3 Wrap up10m
Optional - Install Wget (Only If Needed)10m
1개 연습문제
Week 3 Quiz
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
11개 동영상 (총 26분), 3 readings, 2 quizzes
11개의 동영상
A Few Words From Laurence53
Building a Simple Web Page2m
Retraining the MobileNet Model1m
The Training Function2m
Capturing the Data3m
The Dataset Class2m
Training the Network with the Captured Data1m
Performing Inference4m
Rock Paper Scissors In Code4m
A Conversation with Andrew Ng1m
3개의 읽기 자료
Rock Paper Scissors10m
Exercise Description10m
Wrap up10m
1개 연습문제
Week 4 Quiz
검토
4.7
BROWSER-BASED MODELS WITH TENSORFLOW.JS의 최상위 리뷰
AZ 제공Mar 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 제공Dec 19, 2019
Awesome - elegant in its complex simplicity. Clear explanations, logical curriculum structure, nice and knowledgeable code examples. A must-complete course indeed!
SS 제공Jan 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 제공Feb 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 제공Mar 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 제공Dec 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 제공Dec 17, 2019
Last exercise was really difficult to pass! Probably due to overfitting and not generalising to the the test set.
PG 제공Dec 16, 2019
Great videos and nice coding challenges. Would like some more support on various error messages though
EC 제공Jan 4, 2020
Excellent! well presented and guided through. A good starting point to begin playing with the tech!
RS 제공Feb 19, 2020
Great resource to build practical, real-world examples...easily explained with lots of sample code.
MB 제공Dec 27, 2019
Great introduction on browser-based models. Well presented with great code examples and exercises.
OH 제공Feb 6, 2020
This was an awesome course with everything well explained and with some really cool projects.
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deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
TensorFlow: Data and Deployment 전문 분야 정보
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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