DeepLearning.AI
Browser-based Models with TensorFlow.js
DeepLearning.AI

Browser-based Models with TensorFlow.js

This course is part of TensorFlow: Data and Deployment Specialization

Taught in English

Some content may not be translated

Laurence Moroney

Instructor: Laurence Moroney

43,492 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.8

(966 reviews)

|

94%

Intermediate level

Recommended experience

18 hours (approximately)
Flexible schedule
Learn at your own pace

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

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(966 reviews)

|

94%

Intermediate level

Recommended experience

18 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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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 quizzes1 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 quiz1 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 quiz1 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 quiz1 programming assignment

Instructor

Instructor ratings
4.8 (173 ratings)
Laurence Moroney
DeepLearning.AI
15 Courses482,820 learners

Offered by

DeepLearning.AI

Recommended if you're interested in Software Development

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