Batch Normalization (Procedure)

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Получаемые навыки

Controllable Generation, WGANs, Conditional Generation, Components of GANs, DCGANs

Рецензии

4.7 (оценок: 781)
  • 5 stars
    78.87%
  • 4 stars
    16.38%
  • 3 stars
    2.68%
  • 2 stars
    1.15%
  • 1 star
    0.89%
BN
20 окт. 2020 г.

The course is amazing with an amazing instructor. I really enjoyed the course and thank you so much for making this specialization. A big thanks to deeplearningai team.

AA
1 нояб. 2020 г.

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.

Из урока
Week 2: Deep Convolutional GANs
Learn about different activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images!

Преподаватели

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    Sharon Zhou

    Instructor
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    Eda Zhou

    Curriculum Developer
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    Eric Zelikman

    Curriculum Engineer

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