DeepLearning.AI - Apply Generative Adversarial Networks (GANs)
- Offered byCoursera
Apply Generative Adversarial Networks (GANs) at Coursera Overview
Duration | 26 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Apply Generative Adversarial Networks (GANs) at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 3 in the Generative Adversarial Networks (GANs) Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Basic calculus, linear algebra, stats Grasp of AI, deep learning & CNNs Intermediate Python & experience with DL frameworks (TF / Keras / PyTorch)
- Approx. 26 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish
Apply Generative Adversarial Networks (GANs) at Coursera Course details
- 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.
Apply Generative Adversarial Networks (GANs) at Coursera Curriculum
Week 1: GANs for Data Augmentation and Privacy
Welcome to Course 3
Welcome to Week 1
Overview of GAN Applications
Data Augmentation: Methods and Uses
Data Augmentation: Pros & Cons
GANs for Privacy
GANs for Anonymity
Syllabus
Connect with your mentors and fellow learners on Slack!
(Optional) Automated Data Augmentation
(Optional Notebook) Generative Teaching Networks
(Optional) Talking Heads
(Optional) De-identification
(Optional) GAN Fingerprints
Works Cited
GANs Hippocratic Oath
Week 2: Image-to-Image Translation with Pix2Pix
Welcome to Week 2
Image-to-Image Translation
Pix2Pix Overview
Pix2Pix: PatchGAN
Pix2Pix: U-Net
Pix2Pix: Pixel Distance Loss Term
Pix2Pix: Putting It All Together
Pix2Pix Advancements
(Optional) The Pix2Pix Paper
(Optional Notebook) Pix2PixHD
(Optional Notebook) Super-resolution GAN (SRGAN)
(Optional) More Work Using PatchGAN
(Optional Notebook) GauGAN
Works Cited
Week 3: Unpaired Translation with CycleGAN
Welcome to Week 3
Unpaired Image-to-Image Translation
CycleGAN Overview
CycleGAN: Two GANs
CycleGAN: Cycle Consistency
CycleGAN: Least Squares Loss
CycleGAN: Identity Loss
CycleGAN: Putting It All Together
CycleGAN Applications & Variants
(Optional) The CycleGAN Paper
(Optional) CycleGAN for Medical Imaging
(Optional Notebook) MUNIT
Works Cited
Acknowledgements
Apply Generative Adversarial Networks (GANs) at Coursera Admission Process
Important Dates
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