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.
Browser-based Models with TensorFlow.js
Browser-based Models with TensorFlow.js
This course is part of TensorFlow: Data and Deployment Specialization
Instructor: Laurence Moroney
48,834 already enrolled
Gain insight into a topic and learn the fundamentals.
4.8
(1,005 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 18 hours
Learn at your own pace
94%
Most learners liked this course
What you'll learn
Train and run inference in a browser
Handle data in a browser
Build an object classification and recognition model using a webcam
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
5 assignments
Taught in English
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This course is part of the TensorFlow: Data and Deployment Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
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.
What's included
11 videos9 readings2 assignments1 programming assignment1 app item
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!
What's included
8 videos6 readings1 assignment1 programming assignment
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.
What's included
12 videos7 readings1 assignment1 programming assignment1 ungraded lab
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.
What's included
11 videos5 readings1 assignment1 programming assignment
Instructor
Instructor ratings
4.8 (181 ratings)Offered by
Recommended if you're interested in Software Development
Imperial College London
DeepLearning.AI
Coursera Project Network
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Showing 3 of 1005
VV
5
Reviewed on 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.
DP
5
Reviewed on Jul 3, 2020
Excellent presentation of material and lab examples.
SM
5
Reviewed on May 3, 2021
This course has given me a lot of real world exercises. The lessons are concise yet really helpful to start your web-based AI project.
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Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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