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

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

Instructor: Laurence Moroney

Access provided by L4G Solutions Private Limited

404,058 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

19,730 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

96%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

19,730 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

96%

Most learners liked this course

What you'll 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'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your Software Development expertise

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

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from DeepLearning.AI

There are 4 modules in this course

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 DeepLearning.AI TensorFlow Developer 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 Developer 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.

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...

What's included

4 videos8 readings1 assignment1 programming assignment1 app item1 ungraded lab1 plugin

4 videos Total 15 minutes

Introduction: A conversation with Andrew Ng 3 minutes
A primer in machine learning 3 minutes
The ‘Hello World’ of neural networks 6 minutes
Working through ‘Hello World’ in TensorFlow and Python 3 minutes

8 readings Total 26 minutes

Welcome to the course! 1 minute
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas! 2 minutes
From rules to data 2 minutes
About the notebooks in this course 5 minutes
Lecture Notes Week 1 1 minute
Assignment Troubleshooting Tips 5 minutes
(Optional) Downloading your Notebook and Refreshing your Workspace 5 minutes
Week 1 Resources 5 minutes

1 assignment Total 20 minutes

Week 1 Quiz 20 minutes

1 programming assignment Total 180 minutes

Housing Prices 180 minutes

1 app item Total 1 minute

Intake Survey 1 minute

1 ungraded lab Total 60 minutes

Try it for yourself (Lab 1) 60 minutes

1 plugin Total 4 minutes

Get started with Google Colaboratory (Coding TensorFlow) 4 minutes

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! Check out this conversation between Laurence and Andrew where they discuss it and introduce you to Computer Vision!

What's included

7 videos3 readings1 assignment1 programming assignment2 ungraded labs

7 videos Total 15 minutes

A Conversation with Andrew Ng 2 minutes
An Introduction to computer vision 2 minutes
Writing code to load training data 2 minutes
Coding a Computer Vision Neural Network 2 minutes
Walk through a Notebook for computer vision 3 minutes
Using Callbacks to control training 2 minutes
Walk through a notebook with Callbacks 1 minute

3 readings Total 12 minutes

Exploring how to use data 10 minutes
Labelling the Fashion MNIST data 1 minute
Lecture Notes Week 2 1 minute

1 assignment Total 20 minutes

Week 2 Quiz 20 minutes

1 programming assignment Total 180 minutes

Implementing Callbacks in TensorFlow using the MNIST Dataset 180 minutes

2 ungraded labs Total 75 minutes

Get hands-on with computer vision (Lab 1) 45 minutes
See how to implement Callbacks (Lab 2) 30 minutes

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.

What's included

6 videos3 readings1 assignment1 programming assignment2 ungraded labs

6 videos Total 18 minutes

A conversation with Andrew Ng 2 minutes
What are convolutions and pooling? 3 minutes
Implementing convolutional layers 2 minutes
Implementing pooling layers 4 minutes
Improving the Fashion classifier with convolutions 4 minutes
Walking through convolutions 3 minutes

3 readings Total 12 minutes

Coding convolutions and pooling layers 10 minutes
Learn more about convolutions 1 minute
Lecture Notes Week 3 1 minute

1 assignment Total 20 minutes

Week 3 Quiz 20 minutes

1 programming assignment Total 180 minutes

Improve MNIST with convolutions 180 minutes

2 ungraded labs Total 90 minutes

Try it for yourself (Lab 1) 30 minutes
Experiment with filters and pools (Lab 2) 60 minutes

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!

What's included

9 videos6 readings1 assignment1 programming assignment3 ungraded labs

9 videos Total 28 minutes

A conversation with Andrew Ng 2 minutes
Understanding the tf.data API 6 minutes
Defining a ConvNet to use complex images 3 minutes
Training the ConvNet 2 minutes
Walking through developing a ConvNet 2 minutes
Walking through training the ConvNet 4 minutes
Adding automatic validation to test accuracy 4 minutes
Exploring the impact of compressing images 3 minutes
A conversation with Andrew 2 minutes

6 readings Total 17 minutes

Explore an impactful, real-world solution 3 minutes
Training the neural network 10 minutes
Lecture Notes Week 4 1 minute
[IMPORTANT] Reminder about end of access to Lab Notebooks 2 minutes
Wrap up 0 minutes
Acknowledgments 1 minute

1 assignment Total 20 minutes

Week 4 Quiz 20 minutes

1 programming assignment Total 180 minutes

Handling Complex Images 180 minutes

3 ungraded labs Total 150 minutes

Experiment with the horse or human classifier (Lab 1) 30 minutes
Get hands-on and use validation (Lab 2) 60 minutes
Get hands-on with compacted images (Lab 3) 60 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Instructor ratings

(4,985 ratings)

Laurence Moroney

DeepLearning.AI

22 Courses 595,018 learners

Offered by

DeepLearning.AI

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

5 stars
81.19%
4 stars
15.18%
3 stars
2.40%
2 stars
0.62%
1 star
0.58%

Showing 3 of 19730

Reviewed on Aug 14, 2020

This course is awesome and the way instructor teaches the topic is fantastic.I would definitely recommend this course for Ai enthusiast and tech enthusiast who are interested in learning Tensorflow.

Reviewed on Nov 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!

Reviewed on Mar 8, 2019

Good intro course, but google colab assignments need to be improved. And submitting a jupyter notebook was much more easier, why would I want to login to my google account to be a part of this course?

View more reviews