This course is part of the TensorFlow: Data and Deployment Specialization

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Laurence Moroney

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TensorFlow: Data and Deployment SpecializationDeepLearning.AI

About this Course

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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.
In this first course, you’ll train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam.

This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Course 1 of 4 in the

TensorFlow: Data and Deployment Specialization

Intermediate Level

Basic understanding of JavaScript

Approx. 22 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

What you will 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 will gain

Convolutional Neural Network
Machine Learning
Tensorflow
Object Detection
TensorFlow.js

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 4 in the

TensorFlow: Data and Deployment Specialization

Intermediate Level

Basic understanding of JavaScript

Approx. 22 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Instructor

Instructor rating

4.76/5 (155 Ratings)

Laurence Moroney

Instructor

Lead AI Advocate, Google

336,454 Learners

15 Courses

Offered by

DeepLearning.AI

DeepLearning.AI is an education technology company that develops a global community of AI talent.

Syllabus - What you will learn from this course

Content Rating95%(2,332 ratings)

Week

Week 1

7 hours to complete

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.

7 hours to complete

11 videos (Total 30 min), 7 readings, 5 quizzes

11 videos

Specialization Introduction, A Conversation with Andrew Ng59s

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 Ungraded Labs and Programming Assignments10m

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

Week 2

5 hours to complete

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!

5 hours to complete

8 videos (Total 27 min), 5 readings, 3 quizzes

8 videos

Introduction, A Conversation with Andrew Ng1m

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

tfjs-vis Documentation10m

MNIST Sprite Sheet10m

MNIST Classifier10m

Week 2 Wrap up10m

Exercise Description10m

1 practice exercise

Week 2 Quiz

Week

Week 3

5 hours to complete

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 hours to complete

12 videos (Total 28 min), 6 readings, 3 quizzes

12 videos

Introduction, A Conversation with Andrew Ng1m

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

6 readings

Important Links10m

Toxicity Classifier10m

Classes Supported by MobileNet10m

Image Classification Using MobileNet10m

Linear Model10m

Week 3 Wrap up10m

1 practice exercise

Week 3 Quiz

Week

Week 4

5 hours to complete

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.

5 hours to complete

11 videos (Total 26 min), 3 readings, 3 quizzes

11 videos

Introduction, A Conversation with Andrew Ng1m

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

Reviews

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TOP REVIEWS FROM BROWSER-BASED MODELS WITH TENSORFLOW.JS

by JGDec 19, 2020

Excellent course!!! It is actually a milestone for people like me who have trained models in Jupyter notebooks, but Tensorflow JS is actually a great way for the models to become 'alive'! Thanks!

by EJMar 17, 2021

This course is very practical and interesting. I enjoyed the excitement I got along the way.\n\nIt was modeled to make you pass as long as you want to pass.\n\nThank you Laurence and Andrew.

by AZMar 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. Thanks to all.

by SAJul 7, 2020

course contents are good and explained very well with one problem of audio, audio is not clear and pitch is low but I like this course. as a beginner, this course is best.

View all reviews

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 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. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.

Frequently Asked Questions

When will I have access to the lectures and assignments?
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:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

More questions? Visit the Learner Help Center.

Browser-based Models with TensorFlow.js