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

4.7
stars
906 ratings

Laurence Moroney

36,563 already enrolled

Offered By

TensorFlow: Data and Deployment SpecializationDeepLearning.AI

About this Course

13,544 recent views

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

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. 17 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

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. 17 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.77/5 (163 Ratings)

Laurence Moroney

Instructor

Lead AI Advocate, Google

416,088 Learners

15 Courses

Offered by

DeepLearning.AI

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

Learn more about Coursera for Business

Syllabus - What you will learn from this course

Content Rating95%(2,734 ratings)

Week1

Week 1

5 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.

5 hours to complete

11 videos (Total 30 min), 7 readings, 6 quizzes

11 videos

Specialization Introduction, A Conversation with Andrew Ng59sCourse Introduction, A Conversation with Andrew Ng1mA Few Words From Laurence2mBuilding the Model3mTraining the Model3mFirst Example In Code4mThe Iris Dataset1mReading the Data4mOne-hot Encoding1mDesigning the NN2mIris Classifier In Code6m

7 readings

Getting Your System Ready10mDownloading the Ungraded Labs and Programming Assignments10mYour First Model10mIris Dataset Documentation10mUsing the Web Server10mIris Classifier10mWeek 1 Wrap up10m

2 practice exercises

Quiz 1One-Hot Encoding

Week2

Week 2

4 hours to complete

Image Classification In the Browser

4 hours to complete

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

8 videos

Introduction, A Conversation with Andrew Ng1mCreating a Convolutional Net with JavaScript4mVisualizing the Training Process2mWhat Is a Sprite Sheet?1mUsing the Sprite Sheet2mUsing tf.tidy() to Save Memory1mA Few Words From Laurence24sMNIST Classifier In Code13m

5 readings

tfjs-vis Documentation10mMNIST Sprite Sheet10mMNIST Classifier10mWeek 2 Wrap up10mExercise Description10m

1 practice exercise

Week 2 Quiz

Week3

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 Ng1mA Few Words From Laurence1mPre-trained TensorFlow.js Models49sToxicity Classifier3mToxicity Classifier In Code3mMobileNet49sUsing MobileNet1mTraining Results1mMobileNet Example In Code3mConverting Models to JavaScript4mConverting Models to JavaScript In Code2mLinear Example In Code1m

6 readings

Important Links10mToxicity Classifier10mClasses Supported by MobileNet10mImage Classification Using MobileNet10mLinear Model10mWeek 3 Wrap up10m

1 practice exercise

Week 3 Quiz

Week4

Week 4

4 hours to complete

Transfer Learning with Pre-Trained Models

4 hours to complete

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

11 videos

Introduction, A Conversation with Andrew Ng1mA Few Words From Laurence53sBuilding a Simple Web Page2mRetraining the MobileNet Model1mThe Training Function2mCapturing the Data3mThe Dataset Class2mTraining the Network with the Captured Data1mPerforming Inference4mRock Paper Scissors In Code4mA Conversation with Andrew Ng1m

3 readings

Rock Paper Scissors10mExercise Description10mWrap up10m

1 practice exercise

Week 4 Quiz

Reviews

4.7

197 reviews

5 stars
81.47%
4 stars
14.33%
3 stars
2.64%
2 stars
0.66%
1 star
0.88%

TOP REVIEWS FROM BROWSER-BASED MODELS WITH TENSORFLOW.JS

by VVDec 17, 2019

Great course! A bit of a learning curve on javascript, but really worth it! The exercise in Week 4 was phenomenal! Thanks.

by ZZMay 22, 2021

i like how Laurance give the explanation, but there is something that I think very bad, that is a program assisgment is complicated and takes a lot of time to complete

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 SRMay 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.

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. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Build employee skills, drive business results

Discover Coursera for Business

Browser-based Models with TensorFlow.js

About this Course

What you will learn

Skills you will gain