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

4.7
stars
1,867 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 437 Reviews for Build Basic Generative Adversarial Networks (GANs)

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.

By yzhuang9

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Aug 16, 2021

Sharon's speech is a little bit fast

By Pavan C

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Jul 17, 2023

The instructor is very fast paced.

By SHARMILA V 2

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Dec 15, 2021

course was good and interesting

By Huaiwei C

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Dec 3, 2020

need more coding exercise!!!!

By Charles S

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Jul 7, 2023

Easy to follow.

By Huan T

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Oct 11, 2020

wunderbar

By MoChuxian

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Oct 13, 2020

nice !

By Pema W

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Dec 2, 2022

great

By Vikram N

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May 26, 2021

The course started well but went downhill in week 3. The videos, actually get shorter and the treatment provided to the material related to Wasserstein distance, 1-L Continuity, interpolation and other crucial topics is just superficial. There are not adequate number of quizzes to test yourself. There is insufficient mathematical rigour. And it is too easy to pass the graded assignments without actually understanding the material. The forums are somewhat dead and you need to go to the Slack rooms to ask questions. On slack, it is a case of people linking to other papers rather than providing simple, direct answers. Nobody knows anything for sure. Overall, there is a take-it or leave-it attitude in this course and it is a far cry from Andrew's original ML Course which made Coursera such an attractive learning destination. I do hope the course is improved over time by adding more quizzes, delving deeper into topics (it's okay to have long videos where the instructor explains things slowly) and providing a more mathematically satisfying experience where the foundations are made stronger.

On the positive aspects - the notebooks provided are an excellent starting point to begin your own explorations. And the material is cutting edge.