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

4.7
stars
1,947 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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351 - 375 of 449 Reviews for Build Basic Generative Adversarial Networks (GANs)

By Yash R

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Feb 12, 2022

I don't like that it skips a lot of mathematics behind the concepts. The programming assingments were nice. I would really really like it if they also added mathematical explanation behind the concepts taught. Otherwise it was a nice course.

By Feng T

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Mar 29, 2022

Good course!

My suggestion is that we need to add more detailed examples (with numbers) (not just shown in the assignment) immediately after the introduction of a model, which will significantly help the students to understand the model.

By Ibrahim G

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Oct 20, 2020

The course was very good, only complaint is that assignment w4b was a little vague, in terms of comments on the code and even the fact that no paper or explanation was offered in the course for in depth implementation of the algorithm.

By Rustem G

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Nov 25, 2020

Great material and instructors. Enjoyed watching videos and taking assignments.

Assignments could have been more difficult if we assume most people have taken the deep learning specialization or are familiar with deep learning.

Thanks

By Stijn M

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Jan 13, 2021

I love the explanation and what you actually do in this course. However, if I were to use this to evaluate whether a candidate for a job can work with GANs in practice, I think the complexity for passing the exercises is too low.

By Paul M

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Oct 15, 2020

Nice material, but the assignments are extremely rudimentary (paint by numbers/fill in the blanks). Perhaps you could provide more advanced (even ungraded, if that's the challenge) assignments for folks that want them?

Thanks

By Debdulal D

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Dec 31, 2020

The voice over was pretty fast and hard to understand, so had to do lots of sliding window in video to understand the topics. Otherwise this course is fantastic gateway to understand GAN and it's applicability.

By Sanjay K

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Apr 25, 2022

The Teacher is awesome the way she explains the concepts through great examples. I wish the exercises were a little bit more handson and independent (most of the code structure is already there).

By Greg H

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Aug 24, 2022

Great material. At times, I think there wasn't enough explanation to get the right answers for the assignments, I needed to guess at times and not completely understand what was going on.

By Cameron M

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Oct 6, 2020

Great intro course, the programming assignments were pretty weak in difficulty level, could have had less hand holding there. Excited to get into more high resolution GANs soon!

By Mahmoud T S

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Dec 6, 2020

A little lacking in technical knowledge. You just get to build a GAN and understand bits and pieces about why it works in very simple terms, little mathematics involved.

By handy-mat

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Feb 12, 2023

Lectures were clear, focused & informative. Assignments were well-formed with extremely helpful hints & explanations of the code to be completed. Nice work!

By Deleted A

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Oct 16, 2020

Great course to start building GANs.

I wish more math was included. I realize the math behind this is very complex, and not everyone wants to know about that.

By Shawn

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Nov 28, 2021

Great examples. Wish there were more reading material that bridged the gap between the papers (very detailed) and the slides (good for exposure to material)

By John F

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May 15, 2022

An excellent course. The only area of improvement I can think of would be to get better intuition on the tensor shapes through the model building code.

By Heinz D

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Oct 13, 2020

Great: a motivating teacher and well-structured learning material. It would be cool to provide the slide sets and to eliminate the need to use Slack.

By Rob B

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Apr 7, 2021

Excellent example code and assignments. Overall great course, only suggestion but would be adding a little more depth in the lecture topics.

By Jonas B

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Dec 2, 2020

Good and quite quick course. Assignments very focused on the innovation of the week, which makes them very short and not very demanding.

By Ranajit S

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Oct 14, 2020

The course was too good and knowledgable. But I felt the loss calculation of the disentanglement should have been explained in detail.

By Laiba T

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Jan 5, 2021

There should be some explanation of the assignment's code. The lectures were precise and intresting. I like it. It was informative.

By Priyank N

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Oct 23, 2020

Sharon Nailed it on the insights and the intutions behind every concept discussed and their visual and crisp clarity reasonings

By Michael M

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Oct 19, 2021

often felt I could infer what to do an assignment without understanding why I was doing it but generally great course content

By Aleksei

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Nov 21, 2020

A very good course to understand the basics of how GANs work, but sometimes mathematical explanations were lacking

By Arunava M

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Jan 14, 2021

I think the videos could have been a bit longer and more technically detailed, nonetheless an enjoyable course!

By Siddharta M

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Apr 9, 2023

It's a great course. However it would have been better if there were more videos to explain the coding part.