About this Specialization
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.
From | deeplearning.ai via Coursera |
---|---|
Hours | 1 |
Instructors | Sharon Zhou, Eda Zhou, Eric Zelikman |
Language | English |
Subjects | Programming Data Science |
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Careers
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Research Scientist-Machine Learning $55k
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RESEARCH SCIENTIST (MACHINE LEARNING) $147k
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Machine Learning Scientist Manager $170k
Machine Learning Scientist, Personalization $213k
Courses in this Specialization
Listed in the order in which they should be taken
Starts | Course Information | |
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Jun |
Build Basic Generative Adversarial Networks (GANs) 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... Coursera | deeplearning.ai |
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Jun |
Build Better Generative Adversarial Networks (GANs) 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... Coursera | deeplearning.ai |
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Jun |
Apply Generative Adversarial Networks (GANs) (You were viewing this course) 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... Coursera | deeplearning.ai |
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From | deeplearning.ai via Coursera |
---|---|
Hours | 1 |
Instructors | Sharon Zhou, Eda Zhou, Eric Zelikman |
Language | English |
Subjects | Programming Data Science |
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile (33rd - 99th).
Research Scientist-Machine Learning $55k
Cloud Architect - Azure / Machine Learning $75k
Watson Machine Learning Engineer $81k
Machine Learning Software Developer $103k
Software Engineer (Machine Learning) $116k
Applied Scientist, Machine Learning $130k
Autonomy and Machine Learning Solutions Architect $131k
Applied Scientist - Machine Learning -... $136k
RESEARCH SCIENTIST (MACHINE LEARNING) $147k
Machine Learning Engineer 2 $161k
Machine Learning Scientist Manager $170k
Machine Learning Scientist, Personalization $213k
Similar Courses
Sorted by relevance