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

About this Course
Learner Career Outcomes
24%
28%
Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
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
Learner Career Outcomes
24%
28%
Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
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
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.
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!
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.
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!
Reviews
TOP REVIEWS FROM INTRODUCTION TO TENSORFLOW FOR ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING
Very well organized. Good speakers. Content is comprehensive for a Introductory Course. A little more explanation on Validation versus Testing and on some of the evaluation functions would be helpful.
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?
I just can say that it was an awesome course. The instructors as well as the contents were clear, easy to understand and everything with a focus on how to take the theory and apply it with TensorFlow.
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.
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.

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