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返回到 Build Basic Generative Adversarial Networks (GANs)

學生對 deeplearning.ai 提供的 Build Basic Generative Adversarial Networks (GANs) 的評價和反饋

4.7
1,396 個評分
336 條評論

課程概述

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....

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WM
2020年10月1日

The course provides good insight into the world of GANs. I really enjoyed Sharon's explanations which were deep and easy to understand. I really recommend this course to anyone interested in AI.

MS
2020年10月10日

great course, only teaching what's needed, doesn't push you a lot in the coding assignments, as much as it requires you much more work to understand the codes and the science behind it.

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76 - Build Basic Generative Adversarial Networks (GANs) 的 100 個評論(共 344 個)

創建者 César S

2021年1月18日

Wonderful course for anyone interested in getting an introduction to GANs. All this knowledge will help me get closer to do research on state-of-the-art GAN models. Thank you for creating this material.

創建者 Sebastian P

2020年10月22日

Excellent course, the first good thing is using PyTorch, love it , never had work with this framework and its really nice, second thing is about GANs, amazing topic I really want to learn more about it!

創建者 André L B V

2021年3月4日

Great introduction of GANs. I particularly liked the programming assignments' difficulty (not too easy and not too hard). Also, the instructor is usually very clear and didactic.

創建者 Даниил Д Л

2021年3月28日

Very nice course to start your acquaintance with GANs. Loved the non-obvious mention of ProteinGANs to generate protein structures.

Definetly a recommendation for the novice.

創建者 Wenhui L

2020年10月14日

The course is great with hands-on experiments. The assignments are properly designed to let the learner focus on the most important pieces of the logic in the implementation

創建者 Ashish

2020年11月1日

Good overall introduction to GANs. I really liked how well the sections on Wasserstein Loss and Conditional & Controllable GAN sections were covered in this course.

創建者 Hernandez M K J

2020年12月10日

This course was awesome. Concise, simple and straightforward. The course teaches something very sophisticated but the instructor made it very easy to understand.

創建者 Rafael M

2021年7月27日

Awesome course. Like any other from DeepLearnin.AI, the content is given in a intuitive way, so that you can learn easily. Congratulations for the creators!

創建者 Sebastian K

2020年11月17日

Great course! The programming assignments were a bit short and too easy. The Deep Learning Specialization assignments had the ideal difficulty and length.

創建者 Arvind K V

2020年10月16日

I really like the way he teaches all the concept from scratch. i learn a lot

any one want to learn foundation for GAN i really recommend them this course

創建者 Lambertus d G

2021年1月9日

Sharon rocks! Very clear explanation of quite complicated material makes it relatively easy to understand GANs. Looking forward to starting course 2!

創建者 Nastaran E

2020年11月10日

I really enjoyed taking this course on GANs. It walked me through the concepts in a reasonable speed and provided detailed explanations and insights.

創建者 Yoel S

2021年4月10日

Excellent

Well organized, clarifies terms and concepts, high implementation

quality of assignments, impressively up-to-date on new works (Apr 2021)

創建者 Ryan C

2021年12月13日

This is such a great course. Explanation and guidance throughout the course was excellent. A huge thanks to our lecturer Sharon, Eric, and Eda.

創建者 Aditya A K

2020年12月31日

This course rightly covers the introduction of both Pytorch and GANs so that the natural interest for further courses keeps increasing.

創建者 Rafael P

2020年11月14日

I loved it! The guided notebooks are great to make sure I am not doing any mistake and also providing unit tests in important cells.

創建者 Shubhankar S

2020年11月8日

A really good course to learn about GANs, reading the quoted research papers will help develop a better intuition and understanding.

創建者 Hashan A

2020年10月11日

Good job at explaining theories quickly. The assignments helped to learn pytorch and also to verify the understanding of principles.

創建者 Zahid A

2021年6月14日

One of the Amazing course on the Coursera Platform. Due to these courses I had choose my Final Year Project on GAN. Happy learning.

創建者 Vishnu N

2020年12月12日

Thank you Sharon Zhou and other Instructors for this interesting course on Generative Adversarial Networks (GANs)

ThankYou Coursera.

創建者 Aleks S

2020年10月21日

Good course overall. I don't feel ready to implement GANs after the assignments though because so much of the code was pre-written.

創建者 George N

2020年11月18日

So awesomely taught. Assignments were motivatingly easy and optional advanced material provided for those who want to delve deeper

創建者 Bharath P

2020年10月8日

Nice Course. Lot of depth concepts were really simplified. Better to get good understanding of pytorch to follow Assignemnets well

創建者 Akshai S

2021年1月14日

The course has meticulously designed for easier understanding. One has to complete the assignments to get hands on experience.

創建者 Arkady A

2020年12月26日

Awesome course with clear and straightforward instruction - I felt motivated to complete this 4 week course in just two weeks.