Generative Adversarial Networks (GANs)

About GANs\\n\\nGenerative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs.\\n\\nRooted 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.\\n\\nAbout this Specialization\\n\\nThe 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.\\n\\nBuild 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.\\n\\nAbout you\\n\\nThis Specialization is for software engineers, students, and researchers from any field, who are interested in machine learning and want to understand how GANs work.\\n\\nThis 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.

Created by: Duke University & Princeton University

Language: English

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