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

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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

405,833 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

19,738 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.

19,738 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

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Assessments

4 assignments

Taught in English

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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.

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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 videosTotal 15 minutes

Introduction: A conversation with Andrew Ng3 minutes
A primer in machine learning3 minutes
The ‘Hello World’ of neural networks6 minutes
Working through ‘Hello World’ in TensorFlow and Python3 minutes

8 readingsTotal 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 data2 minutes
About the notebooks in this course5 minutes
Lecture Notes Week 11 minute
Assignment Troubleshooting Tips5 minutes
(Optional) Downloading your Notebook and Refreshing your Workspace5 minutes
Week 1 Resources5 minutes

1 assignmentTotal 20 minutes

Week 1 Quiz20 minutes

1 programming assignmentTotal 180 minutes

Housing Prices180 minutes

1 app itemTotal 1 minute

Intake Survey1 minute

1 ungraded labTotal 60 minutes

Try it for yourself (Lab 1)60 minutes

1 pluginTotal 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 videosTotal 15 minutes

A Conversation with Andrew Ng2 minutes
An Introduction to computer vision2 minutes
Writing code to load training data2 minutes
Coding a Computer Vision Neural Network2 minutes
Walk through a Notebook for computer vision3 minutes
Using Callbacks to control training2 minutes
Walk through a notebook with Callbacks1 minute

3 readingsTotal 12 minutes

Exploring how to use data10 minutes
Labelling the Fashion MNIST data1 minute
Lecture Notes Week 21 minute

1 assignmentTotal 20 minutes

Week 2 Quiz20 minutes

1 programming assignmentTotal 180 minutes

Implementing Callbacks in TensorFlow using the MNIST Dataset180 minutes

2 ungraded labsTotal 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 videosTotal 18 minutes

A conversation with Andrew Ng2 minutes
What are convolutions and pooling?3 minutes
Implementing convolutional layers2 minutes
Implementing pooling layers4 minutes
Improving the Fashion classifier with convolutions4 minutes
Walking through convolutions3 minutes

3 readingsTotal 12 minutes

Coding convolutions and pooling layers10 minutes
Learn more about convolutions1 minute
Lecture Notes Week 31 minute

1 assignmentTotal 20 minutes

Week 3 Quiz20 minutes

1 programming assignmentTotal 180 minutes

Improve MNIST with convolutions180 minutes

2 ungraded labsTotal 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 videosTotal 28 minutes

A conversation with Andrew Ng2 minutes
Understanding the tf.data API6 minutes
Defining a ConvNet to use complex images3 minutes
Training the ConvNet2 minutes
Walking through developing a ConvNet2 minutes
Walking through training the ConvNet4 minutes
Adding automatic validation to test accuracy4 minutes
Exploring the impact of compressing images3 minutes
A conversation with Andrew2 minutes

6 readingsTotal 17 minutes

Explore an impactful, real-world solution3 minutes
Training the neural network10 minutes
Lecture Notes Week 41 minute
[IMPORTANT] Reminder about end of access to Lab Notebooks2 minutes
Wrap up0 minutes
Acknowledgments1 minute

1 assignmentTotal 20 minutes

Week 4 Quiz20 minutes

1 programming assignmentTotal 180 minutes

Handling Complex Images180 minutes

3 ungraded labsTotal 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

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Instructor

Instructor ratings

(4,986 ratings)

Laurence Moroney

DeepLearning.AI

22 Courses606,515 learners

Offered by

DeepLearning.AI

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Reviewed on Sep 25, 2020

awesome stuffs are presented here and lab are those thing which teaches me a lot, between this how can i forgot Prof. Lawrence and Andrew who made these things out of the box. thanks deeplearning.ai

Reviewed on Jan 13, 2020

Learn alot from this course, specially the steps required from importing data to train data from simple to real world examples. Further explanation of each line of code also demonstrate thoroughly.

Reviewed on Jul 8, 2022

The guidance provided in this course including the course content is exceptional. Mixing programming with explanation of underlying mathematics and terms was truly helpful. Kudos to the entire team.

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