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Build Basic Generative Adversarial Networks (GANs)
  • Level Professional
  • المدة 30 ساعات hours
  • الطبع بواسطة DeepLearning.AI
  • Offered by

عن

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.

الوحدات

Intro to GANs

    • 10 Videos
    • 6 Readings
    • 1 ExternalTool
    • 1 Lab
    • 1 Programming
Show info about module content

1 Programming

  • Your First GAN

1 External Tool

  • Intake Survey

1 Labs

  • (Optional) Intro to PyTorch

10 Videos

  • Welcome to the Specialization
  • Welcome to Week 1
  • Generative Models
  • Real Life GANs
  • Intuition Behind GANs
  • Discriminator
  • Generator
  • BCE Cost Function
  • Putting It All Together
  • (Optional) Intro to PyTorch

6 Readings

  • Syllabus
  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!
  • Check out some non-existent people!
  • (Optional) Lecture Notes W1
  • Works Cited
  • How to Refresh your Workspace

Deep Convolutional GANs

    • 9 Videos
    • 5 Readings
    • 1 Programming
Show info about module content

1 Programming

  • Deep Convolutional GAN (DCGAN)

9 Videos

  • Welcome to Week 2
  • Activations (Basic Properties)
  • Common Activation Functions
  • Batch Normalization (Explained)
  • Batch Normalization (Procedure)
  • Review of Convolutions
  • Padding and Stride
  • Pooling and Upsampling
  • Transposed Convolutions

5 Readings

  • (Optional) A Closer Look at Transposed Convolutions
  • (Optional) Lecture Notes W2
  • (Optional) The DCGAN Paper
  • (Optional Notebook) GANs for Video
  • Works Cited

Wasserstein GANs with Gradient Penalty

    • 7 Videos
    • 5 Readings
    • 1 Lab
    • 1 Programming
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1 Programming

  • WGAN

1 Labs

  • (Optional) SN-GAN

7 Videos

  • Welcome to Week 3
  • Mode Collapse
  • Problem with BCE Loss
  • Earth Mover’s Distance
  • Wasserstein Loss
  • Condition on Wasserstein Critic
  • 1-Lipschitz Continuity Enforcement

5 Readings

  • (Optional) Lecture Notes W3
  • (Optional Notebook) ProteinGAN
  • (Optional) The WGAN and WGAN-GP Papers
  • (Optional) WGAN Walkthrough
  • Works Cited

Conditional GAN & Controllable Generation

    • 9 Videos
    • 6 Readings
    • 1 Lab
    • 2 Programming
Show info about module content

2 Programming

  • Conditional GAN
  • Controllable Generation

1 Labs

  • (Optional) InfoGAN

9 Videos

  • Welcome to Week 4
  • Conditional Generation: Intuition
  • Conditional Generation: Inputs
  • Controllable Generation
  • Vector Algebra in the Z-Space
  • Challenges with Controllable Generation
  • Classifier Gradients
  • Disentanglement
  • Conclusion of Course 1

6 Readings

  • (Optional) The Conditional GAN Paper
  • (Optional) Lecture Notes W4
  • [IMPORTANT] Reminder about end of access to Lab Notebooks
  • (Optional) An Example of a Controllable GAN
  • Works Cited
  • Acknowledgments

Auto Summary

Explore the fascinating world of Generative Adversarial Networks (GANs) with the DeepLearning.AI Specialization on Coursera. Taught by AI experts, this course covers GAN fundamentals, multiple architectures, and practical implementation using PyTorch. It's perfect for learners of all levels, offering a comprehensive understanding of GANs, their applications, and social implications. Available subscription options include Starter, Professional, and Paid. Dive into this 1800-minute course to master GANs and apply them to your projects!

Sharon Zhou
Instructors

Sharon Zhou

Eda Zhou
Instructors

Eda Zhou

Eric Zelikman
Instructors

Eric Zelikman