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Learner Reviews & Feedback for Build Basic Generative Adversarial Networks (GANs) by DeepLearning.AI

4.7
stars
1,946 ratings

About the Course

In this course, you will: - Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research....

Top reviews

KM

Jul 20, 2023

Helped me clarify the some of key principles and theories behind GAN and bit of history... The references/additional study materials are very useful, if you want to dig deep into. Overall very pleased

HL

Mar 10, 2022

Great introductory to GANs, focused on the building blocks to neural net/ GANs, and a bit of frequently used models. Might need a small update on what's considered "state-of-the-art" in the course.

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376 - 400 of 449 Reviews for Build Basic Generative Adversarial Networks (GANs)

By Nicholas M C

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Mar 2, 2021

It would be better if the assignments provided much less of the code, so that people could struggle more.

By Mahmoud S E

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Jun 11, 2022

Critic lessons need to be explained more in details. but overall great course with great instructor.

By ROCHETTE P

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Mar 26, 2024

Great, missing more details on how to tune but explanations are very clear and labs are top quality

By Suvojyoti C

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Dec 3, 2020

Very exciting course content! Only if could give a primer on PyTorch - that would be awesome

By Harry_G

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Jan 21, 2023

The content is good for beginners who have little background, but the practice is too easy

By Yudun W

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Dec 18, 2020

A very easy to understand guide for those who are interested in how GAN generally works!

By Aishwarya S M

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May 31, 2023

The course was so useful. Excited to complete the next one and learn more about GANs.

By Alfredo A

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Dec 10, 2021

Good intro to the concept felt that some of the excercises were too explicit

By Nicola P

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Apr 4, 2021

Exceptional theoretical part, but mandatory assignments are way too simple

By Venu V

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Dec 18, 2020

More help (and annotations) on the code beyond start/end blocks would help

By AlexanderV

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Oct 10, 2021

Nice course, however with a clear focus on computer vision applications.

By Niraj S

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Nov 17, 2020

Loving it so far. Kudos to Eda Zhou. She is an excellent instructor.

By Oguzcan B

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Mar 29, 2021

It was very sufficient way to learn Basics of GANs for me.

By Karan S

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Oct 22, 2020

It would have been nice to have the course in tensorflow.

By Samuel h

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Oct 9, 2020

hope the tasks could be more challenging with more hints.

By Ernesto D P H

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Sep 13, 2022

Great course, I learned a lot. Teacher goes a bit fast.

By Guorui S

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May 22, 2023

Pretty good, but I wish it could contain more detail.

By John U

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Feb 16, 2021

Great introduction to GAN's and a dive into PyTorch

By Mohamed M F

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Nov 9, 2020

course needs more math, but overall it is amazing.

By Thomson T G

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Feb 18, 2021

great but programming assignments felt too simple

By Joris G

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Feb 7, 2021

Exercises could have been a bit more challenging.

By Sanjay D

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Oct 1, 2020

Course concepts gets complicates as you progress.

By Luv b

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Oct 17, 2020

Good course. But still, I left with some doubts

By Abhishek K

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Aug 13, 2023

The instructor could have better pronunciation

By Rahul P

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Dec 24, 2020

Best Basic Course on Generative Models.