The 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. The specialization covers social implications, including bias in machine learning and ways to detect it, privacy preservation, and more. With this specialization, learners will build a comprehensive knowledge base and gain hands-on experience in GANs.
What You’ll Learn
- Understand GAN components
- Build basic GANs using PyTorch
- Build advanced DCGANs with convolutional layers
- Control your GAN and build conditional GAN
- Compare generative models
- Use FID method to assess GAN fidelity and diversity
- Detect bias in GAN
- Implement StyleGAN techniques
- Use GANs for data augmentation and privacy preservation
- Survey GAN applications
- Examine and build Pix2Pix and CycleGAN for image translation
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Skills You’ll Gain
- Category: Components of GANs
- Category: WGANs
- Category: DCGANs
- Category: Controllable Generation
- Category: Conditional Generation
- Category: StyleGANs
- Category: GAN Evaluation
- Category: Pros and Cons of GANs
- Category: GANs Alternatives
- Category: Bias in GANs
- Category: Convolutional Neural Network
- Category: Image-to-Image Translation
- Category: CycleGANs
- Category: Privacy Preservation
- Category: Pix2Pix
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Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. Rooted in game theory, GANs have wide-spread applications, from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserving privacy and creating state-of-the-art images. The GANs Specialization offers an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their projects, even without prior familiarity with advanced math and machine learning research.
- Build a comprehensive knowledge base and gain hands-on experience in GANs
- Train your own model using PyTorch and create images
- Evaluate various advanced GAN techniques
Course 1: In this course, you will understand the fundamental components of GANs, build basic GANs using PyTorch, build advanced DCGANs with convolutional layers, control your GAN, and build conditional GAN.
Course 2: This course covers challenges in evaluating GANs, comparing generative models, using FID method to assess GAN fidelity and diversity, understanding and detecting bias in GANs, and implementing state-of-the-art StyleGAN techniques.
Course 3: Explore GAN applications for data augmentation and privacy preservation, survey different GAN applications, and build Pix2Pix and CycleGAN for image translation.
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