What you will learn
Understand GAN components and build basic GANs using PyTorch.
Develop advanced Deep Convolutional GANs (DCGANs) utilizing convolutional layers.
Implement Conditional GANs (cGANs) for controlled image generation.
Evaluate GAN performance using metrics like Fréchet Inception Distance (FID).
Detect and address bias in GANs and implement StyleGAN techniques.
Apply GANs for data augmentation, privacy preservation, and image-to-image translation using Pix2Pix and CycleGAN.
Program Overview
Build Basic Generative Adversarial Networks (GANs)
⏱️29 hours
- Learn the fundamental components of GANs.
- Build a basic GAN using PyTorch.
- Develop DCGANs with convolutional layers.
- Implement Conditional GANs for controlled outputs.
Build Better Generative Adversarial Networks (GANs)
⏱️24 hours
- Assess challenges in evaluating GANs.
- Use FID to evaluate GAN fidelity and diversity.
- Identify and detect bias in GANs.
- Implement techniques associated with StyleGANs.
Apply Generative Adversarial Networks (GANs)
⏱️24 hours
Explore applications of GANs in data augmentation and privacy.
Implement Pix2Pix for paired image-to-image translation.
Implement CycleGAN for unpaired image-to-image translation.
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Job Outlook
Proficiency in GANs is valuable for roles such as Machine Learning Engineer, Computer Vision Engineer, and AI Researcher.
Skills acquired in this specialization are applicable across various industries, including technology, healthcare, and entertainment.
Completing this specialization can enhance your qualifications for positions that require expertise in generative models and deep learning.
Specification: Generative Adversarial Networks (GANs) Specialization
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