This course is part of the DeepLearning.AI TensorFlow Developer Professional Certificate

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Laurence Moroney

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DeepLearning.AI TensorFlow Developer Professional CertificateDeepLearning.AI

About this Course

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If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. 
The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

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Earn a Certificate upon completion

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Course 1 of 4 in the

DeepLearning.AI TensorFlow Developer

Intermediate Level

Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

Approx. 18 hours to complete

English

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

What you will learn

Learn best practices for using TensorFlow, a popular open-source machine learning framework
Build a basic neural network in TensorFlow
Train a neural network for a computer vision application
Understand how to use convolutions to improve your neural network

Skills you will gain

Computer Vision
Tensorflow
Machine Learning

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

DeepLearning.AI TensorFlow Developer

Intermediate Level

Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

Approx. 18 hours to complete

English

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

Instructor

Instructor rating

4.83/5 (4,350 Ratings)

Laurence Moroney

Instructor

Lead AI Advocate, Google

374,083 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 Rating96%(38,883 ratings)

Week

Week 1

3 hours to complete

A New Programming Paradigm

Welcome to this course on going from Basics to Mastery of TensorFlow. We're excited you're here! In Week 1, you'll get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along. To get started, check out the first video, a conversation between Andrew and Laurence that sets the theme for what you'll study...

3 hours to complete

4 videos (Total 16 min), 7 readings, 4 quizzes

4 videos

Introduction: A conversation with Andrew Ng3m

A primer in machine learning3m

The ‘Hello World’ of neural networks5m

Working through ‘Hello World’ in TensorFlow and Python3m

7 readings

Where to find the notebooks for this course5m

From rules to data2m

FAQs about Google Colab (optional)10m

Try it for yourself (Lab 1)20m

Assignment Troubleshooting Tips5m

(Optional) Downloading your Notebook and Refreshing your Workspace5m

Week 1 Resources10m

1 practice exercise

Week 1 Quiz30m

Week

Week 2

5 hours to complete

Introduction to Computer Vision

Welcome to week 2 of the course! In week 1 you learned all about how Machine Learning and Deep Learning is a new programming paradigm. This week you’re going to take that to the next level by beginning to solve problems of computer vision with just a few lines of code!

5 hours to complete

7 videos (Total 15 min), 5 readings, 3 quizzes

7 videos

A Conversation with Andrew Ng2m

An Introduction to computer vision2m

Writing code to load training data2m

Coding a Computer Vision Neural Network2m

Walk through a Notebook for computer vision3m

Using Callbacks to control training1m

Walk through a notebook with Callbacks1m

5 readings

Exploring how to use data10m

The structure of Fashion MNIST data1m

See how it's done

Get hands-on with computer vision (Lab 1)1h

See how to implement Callbacks (Lab 2)30m

1 practice exercise

Week 2 Quiz30m

Week

Week 3

5 hours to complete

Enhancing Vision with Convolutional Neural Networks

Welcome to week 3! In week 2 you saw a basic Neural Network for Computer Vision. It did the job nicely, but it was a little naive in its approach. This week we’ll see how to make it better, as discussed by Laurence and Andrew here.

5 hours to complete

6 videos (Total 19 min), 5 readings, 3 quizzes

6 videos

A conversation with Andrew Ng1m

What are convolutions and pooling?2m

Implementing convolutional layers1m

Implementing pooling layers4m

Improving the Fashion classifier with convolutions5m

Walking through convolutions3m

5 readings

Coding convolutions and pooling layers10m

Learn more about convolutions1m

Getting hands-on, your first ConvNet

Try it for yourself (Lab 1)1h

Experiment with filters and pools (Lab 2)1h

1 practice exercise

Week 3 Quiz30m

Week

Week 4

5 hours to complete

Using Real-world Images

Last week you saw how to improve the results from your deep neural network using convolutions. It was a good start, but the data you used was very basic. What happens when your images are larger, or if the features aren’t always in the same place? Andrew and Laurence discuss this to prepare you for what you’ll learn this week: handling complex images!

5 hours to complete

9 videos (Total 27 min), 10 readings, 3 quizzes

9 videos

A conversation with Andrew Ng2m

Understanding ImageDataGenerator4m

Defining a ConvNet to use complex images2m

Training the ConvNet2m

Walking through developing a ConvNet2m

Walking through training the ConvNet3m

Adding automatic validation to test accuracy4m

Exploring the impact of compressing images3m

A conversation with Andrew1m

10 readings

Explore an impactful, real-world solution3m

Designing the neural network

Train the ConvNet with ImageDataGenerator

Exploring the solution

Training the neural network10m

Experiment with the horse or human classifier (Lab 1)1h

Get hands-on and use validation (Lab 2)30m

Get Hands-on with compacted images (Lab 3)30m

Wrap up

Acknowledgments5m

1 practice exercise

Week 4 Quiz30m

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TOP REVIEWS FROM INTRODUCTION TO TENSORFLOW FOR ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING

by CKApr 7, 2020

This course is helpful in bridging the gap between theory and implementation part. Great course looking forward to complete the specialization as well. Thanks Laurence , I enjoy your teachings.

by NIOct 18, 2020

Great course to get started with building Convolutional Neural Networks in Keras for building Image Classifiers. This is probably the best way to get beginners into Deep Learning for Computer Vision.

by OKAug 5, 2020

My first course on TEnsor and i have nothing to say but this course is a must for everyone who wants to know more about Tensor. It establishes a strong background one could lay advanced knowledge on

by OONov 26, 2020

I give this course 5 stars because of what I'm being able to learn within just a little amount of time. I would highly recommend this course to anyone who wishes to participate, it worth the effort!

View all reviews

About the DeepLearning.AI TensorFlow Developer Professional Certificate

TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.

In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate. Ready to deploy your models to the world? Learn how to go live with your models with the TensorFlow: Data and Deployment Specialization. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.

Frequently Asked Questions

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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:
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 Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, 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.

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Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning