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Sharon Zhou, Eda Zhou, and Eric Zelikman

About GANs

Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs.

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About GANs

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.

About this Specialization

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.

About you

This Specialization is for software engineers, students, and researchers from any field, who are interested in machine learning and want to understand how GANs work.

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.

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What's inside

Three courses

Build Basic Generative Adversarial Networks (GANs)

(0 hours)
In this course, you will learn about GANs, their applications, and the intuition behind their fundamental components. You will explore and implement multiple GAN architectures, including conditional GANs capable of generating examples from determined categories.

Build Better Generative Adversarial Networks (GANs)

(0 hours)
In this course, you will learn to assess the challenges of evaluating GANs and compare different generative models. You will also 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 StyleGANs.

Apply Generative Adversarial Networks (GANs)

(0 hours)
In this course, you will explore the applications of GANs, including data augmentation, privacy, and anonymity. You will also learn how to implement Pix2Pix and CycleGAN, two popular GAN architectures. This course is designed for learners of all levels who are interested in learning about GANs.

Learning objectives

  • Understand gan components, build basic gans using pytorch and advanced dcgans using convolutional layers, control your gan and build conditional gan
  • Compare generative models, use fid method to assess gan fidelity and diversity, learn to detect bias in gan, and implement stylegan techniques
  • Use gans for data augmentation and privacy preservation, survey gans applications, and examine and build pix2pix and cyclegan for image translation

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