WHAT YOU WILL LEARN
Perceive GAN parts, construct fundamental GANs utilizing PyTorch and superior DCGANs utilizing convolutional layers, management your GAN and construct conditional GAN
Examine generative fashions, use FID technique to evaluate GAN constancy and variety, study to detect bias in GAN, and implement StyleGAN methods
Use GANs for knowledge augmentation and privateness preservation, survey GANs functions, and study and construct Pix2Pix and CycleGAN for picture translation
SKILLS YOU WILL GAIN
- Generator
- Picture-to-Picture Translation
- glossary of laptop graphics
- Discriminator
- Generative Adversarial Networks
- Controllable Era
- WGANs
- Conditional Era
- Parts of GANs
- DCGANs
- Bias in GANs
- StyleGANs
About this Specialization
About GANs
Generative Adversarial Networks (GANs) are highly effective machine studying fashions able to producing sensible picture, video, and voice outputs.
Rooted in sport principle, GANs have wide-spread software: from bettering cybersecurity by preventing towards adversarial assaults and anonymizing knowledge to protect privateness to producing state-of-the-art pictures, colorizing black and white pictures, rising picture decision, creating avatars, turning 2D pictures to 3D, and extra.
About this Specialization
The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization offers an thrilling introduction to picture era with GANs, charting a path from foundational ideas to superior methods by means of an easy-to-understand strategy. It additionally covers social implications, together with bias in ML and the methods to detect it, privateness preservation, and extra.
Construct a complete information base and acquire hands-on expertise in GANs. Practice your individual mannequin utilizing PyTorch, use it to create pictures, and consider a wide range of superior GANs.
About you
This Specialization is for software program engineers, college students, and researchers from any discipline, who’re focused on machine studying and need to perceive how GANs work.
This Specialization offers an accessible pathway for all ranges of learners trying to break into the GANs house or apply GANs to their very own initiatives, even with out prior familiarity with superior math and machine studying analysis.
Utilized Studying Venture
Course 1: On this course, you’ll perceive the elemental parts of GANs, construct a fundamental GAN utilizing PyTorch, use convolutional layers to construct superior DCGANs that processes pictures, apply W-Loss operate to resolve the vanishing gradient downside, and discover ways to successfully management your GANs and construct conditional GANs.
Course 2: On this course, you’ll perceive the challenges of evaluating GANs, examine totally different generative fashions, use the Fréchet Inception Distance (FID) technique to judge the constancy and variety of GANs, determine sources of bias and the methods to detect it in GANs, and study and implement the methods related to the state-of-the-art StyleGAN.
Course 3: On this course, you’ll use GANs for knowledge augmentation and privateness preservation, survey extra functions of GANs, and construct Pix2Pix and CycleGAN for picture translation.
There are 3 Courses in this Specialization
Apply Generative Adversarial Networks (GANs)
In this course, you will:
- Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa) - Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures - Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one 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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