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Learner Reviews & Feedback for Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning by DeepLearning.AI

4.8
stars
19,640 ratings

About the 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....

Top reviews

OS

Jan 26, 2023

I really liked the course. It was well explained and very interactive. I would like to continue the rest of the courses in the course if you allow me. Thank you. The course has been of great use to me

AS

Mar 9, 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?

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3176 - 3200 of 4,001 Reviews for Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

By Petruk P

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Apr 17, 2020

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Apr 4, 2020

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Feb 14, 2020

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By Raman M

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By R H

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Sep 13, 2019

Great introduction to using Tensorflow to implement convolutional networks.

I took the Stanford course by Andrew Ng first, so many of the concepts were very familiar - in some cases, the detail was just a little bit shallow - probably to avoid interfering with getting on with implementation - but this course certainly had references outside the course to some more detailed information on topics like how convolutions help identify features or the learning factor.

The jupiter notebooks were great in that you don't need to worry about the environment much - it's already set up - a big worry for me for many of these types of courses. But there were quirks, and a few times I (and some of the other students) could get tripped up for a little while. If you are a developer like I used to be, then troubleshooting and debugging environment/code issues is a small hurdle though.

Kudos to the instructors and those that set up the course - this is otherwise very hard material to teach and set up good "hands on" evaluation, which they did really well, a couple kinks aside.