Generative Adversarial Networks (Gans) Specialization

Understand GAN components, build basic GANs using PyTorch together with advanced DCGANs using convolutional layers, control your GAN in addition to construct conditional GAN

Compare generative models, role FID method to assess GAN fidelity together with diversity, learn to detect bias in GAN, in addition to implement StyleGAN techniques

Use GANs for data augmentation too privacy preservation, survey GANs applications, too test in addition to construct Pix2Pix and CycleGAN for image translation

About GANs

Generative Adversarial Networks (GANs) are powerful motorcar learning models capable of generating realistic picture, video, too phonation outputs. 

Rooted inward game theory, GANs accept broad-spread application: from improving cybersecurity by fighting against adversarial attacks in addition to anonymizing data to save privacy to generating land-of-the-fine art images, colorizing black as well as white images, increasing image resolution, creating avatars, turning 2D images to 3D, too more than. 

About this Specialization

The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to ikon generation alongside GANs, charting a path from foundational concepts to advanced techniques through an tardily-to-empathise approach. It too covers social implications, including bias inwards ML too the ways to discover it, privacy preservation, too more.

Build a comprehensive cognition base of operations as well as reach hands-on experience inwards GANs. Train your own model using PyTorch, function it to create images, too evaluate a diverseness of advanced GANs. 

About you

This Specialization is for software engineers, students, together with researchers from whatsoever plain, who are interested inward machine learning and want to sympathize how GANs go.

This Specialization provides an accessible pathway for all levels of learners looking to suspension into the GANs infinite or utilise GANs to their ain projects, fifty-fifty without prior familiarity with advanced math as well as machine learning inquiry.

Course i: In this form, y'all volition sympathise the key components of GANs, make a basic GAN using PyTorch, function convolutional layers to construct advanced DCGANs that processes images, use west-Loss office to solve the vanishing gradient job, as well as larn how to effectively command your GANs and make conditional GANs.

 Course 2: In this course, yous volition empathise the challenges of evaluating GANs, compare dissimilar generative models, function the Fréchet Inception Distance (FID) method to evaluate the fidelity as well as variety of GANs, identify sources of bias together with the ways to discover it in GANs, as well as acquire and implement the techniques associated with the country-of-the-art StyleGAN.

Course 3: In this form, yous will use GANs for data augmentation and privacy preservation, survey more applications of GANs, in addition to make Pix2Pix and CycleGAN for image translation.