explanation
Generative Adversarial Network (GAN) Specialization A training course for adversarial GAN networks (or GANs). GAN is a powerful machine learning model that can generate realistic images, videos, and sounds. This game theory model originated from… but today its applications are very wide – from improving cybersecurity and anonymized data to protecting privacy, creating artistic images, colorful images, black and white images, increasing resolution, etc. Create an avatar, convert a photo from 2D to 3D, and many others have done just that. This course will provide you with a level of knowledge of GAN-friendly image creation and fundamental concepts with an advanced technical upgrade, as well as engineers, software, and more. It’s ideal for students and researchers in any field who are interested in machine learning and understand how GANs work.
The course is divided into three sub-periods. In the first part of . In the Stem GAN upload section, you will understand that GAN is simple using modules, if you want to build it in PyTorch, the layer design for DCGAN advanced configuration that can process images and apply W-Loss functions to use. How to build a GAN, conditional statements are probably familiar. The second part assigned us the task of evaluating GANs, during which we demonstrated how to use FID methods to evaluate truth and model diversity to compare different GAN models. You might be familiar with detecting biased resources and implementing various techniques related to StyleGAN. The final section is devoted to the practical use of GANs, which enhances data, privacy protection and makes Pix2Pix and CycleGAN transform images and other uses.
What do you learn?
Understand Gans components and build simple Gans using PyTorch and advanced dcgan.
Comparison of manufacturer models, using the Fréchet Inception Distance-FID method, implementing ERBI diagnostics and StyleGAN technology.
Use GANs to power data privacy mapping applications, testing and deploying Pix2Pix and CycleGAN for image translation.
What skills do you acquire?
Generative Adversarial Network (GAN)
Productive and interpretive photos to photos.
Controlled and conditional production
WGAN, Dcgan, and StyleGAN
ERBI in GANs
and…
Generative Adversarial Network (GAN) Specialization Specification
Publisher: Coursera
Instructors: Sharon Zhou, Eda Zhou, Eric Zelikman
Language:English
Training level: Intermediate
Quantity: 3 courses
Course Duration: The suggested duration is 9 hours per week, approximately 3 months.
procedure
- Building a basic generative adversarial network (GAN)
- Building better generative adversarial networks (GANs)
- Application of generative adversarial network (GAN)
precondition
- Learners should have working knowledge of AI, deep learning, and convolutional neural networks. You should have intermediate Python skills and experience with a deep learning framework (TensorFlow, Keras, or PyTorch). Learners should be proficient in basic calculus, linear algebra, and statistics.
- We recommend that you complete the following: Deep Learning Specialization Before starting GAN specialization.
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sample video
installation manual
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Subtitles: English
Quality: 720p
download link
Download Coursera – Generative Adversarial Networks (GAN) Specialty 2021-2
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635MB