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
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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
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- 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
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Reviewed on Apr 5, 2020
It's a good hands-on exercise. I like to see more link to keras api document when we introduce new function in keras. However, Tensorflow document regarding keras api is yet in complete. Thank you.
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 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.
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