About this Course

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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Basic understanding of JavaScript

Approx. 12 hours to complete

Suggested: 4 weeks of study, 4-5 hours/week...

English

Subtitles: English

What you will learn

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    Train and run inference in a browser

  • Check

    Handle data in a browser

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    Build an object classification and recognition model using a webcam

Skills you will gain

Convolutional Neural NetworkMachine LearningTensorflowObject DetectionTensorFlow.js

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Basic understanding of JavaScript

Approx. 12 hours to complete

Suggested: 4 weeks of study, 4-5 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1

Week 1

5 hours to complete

Introduction to TensorFlow.js

5 hours to complete
11 videos (Total 30 min), 7 readings, 3 quizzes
11 videos
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
7 readings
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
2 practice exercises
Quiz 1
One-Hot Encoding
Week
2

Week 2

4 hours to complete

Image Classification In the Browser

4 hours to complete
8 videos (Total 27 min), 5 readings, 2 quizzes
8 videos
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 Laurence24s
MNIST Classifier In Code13m
5 readings
tjs-vis Documentation10m
MNIST Sprite Sheet10m
MNIST Classifier10m
Week 2 Wrap up10m
Exercise Description10m
1 practice exercise
Week 2 Quiz
Week
3

Week 3

5 hours to complete

Converting Models to JSON Format

5 hours to complete
12 videos (Total 28 min), 7 readings, 2 quizzes
12 videos
A Few Words From Laurence1m
Pre-trained TensorFlow.js Models49s
Toxicity Classifier3m
Toxicity Classifier In Code3m
MobileNet49s
Using MobileNet1m
Training Results1m
MobileNet Example In Code3m
Converting Models to JavaScript4m
Converting Models to JavaScript In Code2m
Linear Example In Code1m
7 readings
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
1 practice exercise
Week 3 Quiz
Week
4

Week 4

4 hours to complete

Transfer Learning with Pre-Trained Models

4 hours to complete
11 videos (Total 26 min), 3 readings, 2 quizzes
11 videos
A Few Words From Laurence53s
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
3 readings
Rock Paper Scissors10m
Exercise Description10m
Wrap up10m
1 practice exercise
Week 4 Quiz
4.6

26 Reviews

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Top reviews from Browser-based Models with TensorFlow.js

By ZBDec 20th 2019

Awesome - elegant in its complex simplicity. Clear explanations, logical curriculum structure, nice and knowledgeable code examples. A must-complete course indeed!

By SSJan 2nd 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.

Instructor

Image of instructor, Laurence Moroney

Laurence Moroney

AI Advocate
Google Brain

About deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

About the TensorFlow: Data and Deployment Specialization

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 model. 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, use APIs to control how data splitting, and process all types of unstructured data. 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 AI. 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....
TensorFlow: Data and Deployment

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.