Azmine Toushik Wasi - Certificate
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 application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, turning 2D images to 3D, and more.
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.
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Course 1: In this course, you will understand the fundamental components of GANs, build a basic GAN using PyTorch, use convolutional layers to build advanced DCGANs that processes images, apply W-Loss function to solve the vanishing gradient problem, and learn how to effectively control your GANs and build conditional GANs.
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Course 2: In this course, you will understand the challenges of evaluating GANs, compare different generative models, use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs, identify sources of bias and the ways to detect it in GANs, and learn and implement the techniques associated with the state-of-the-art StyleGAN.
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Course 3: In this course, you will use GANs for data augmentation and privacy preservation, survey more applications of GANs, and build Pix2Pix and CycleGAN for image translation.
4.7stars - 1,653 ratings
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
4.7stars - 573 ratings
In this course, you will:
- Assess the challenges of evaluating GANs and compare different generative models
- Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs
- Identify sources of bias and the ways to detect it in GANs
- Learn and implement the techniques associated with the state-of-the-art StyleGANs
4.8stars - 459 ratings
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.
Instructor Computer Science, Stanford University
Curriculum Developer
Curriculum Engineer
50,046 Learners
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