Generative Adversarial Networks (GANs) were first introduced by Ian Goodfellow and his colleagues in June 2014, where two neural networks contest with each other in a game. With the invention of GANs, Generative Models had started showing promising results in generating realistic images. GANs has shown tremendous success in Computer Vision.
In this short talk, I will describe the basic idea behind adversarial learning in its most basic form. After that I will show live some code and how easy is to implement with Keras GANs. I will also point out which Keras features make the implementation easy and point out what resources are available for learning more about this topic.
*No previous Keras experience is required (although it would be very helpful) as I will try to highlight the main components of the GANs in the Keras code to make it understandable also for participants not familiar with TensorFlow.
Event Wrap-up:
You can find the recording of the event on our YouTube channel and the slides and code examples on GitHub. As promised, here are some additional resources that Umberto would like to share with you:
Google Developer Expert in Machine Learning
Umberto is an Award-winning artificial intelligence researcher, lecturer, advisor, and mentor with 20 years of experience in solving complex problems with innovative and advanced technologies. As a lecturer, he helps universities and research groups to learn and use machine learning techniques in their research projects and publications. He is responsible for artificial intelligence in large E…
Umberto is an Award-winning artificial intelligence researcher, lecturer, advisor, and mentor with 20 years of experience in solving complex problems with innovative and advanced technologies. As a lecturer, he helps universities and research groups to learn and use machine learning techniques in their research projects and publications. He is responsible for artificial intelligence in large European-funded projects with large international consortia. He brings his passion and experience as a long-distance runner into research and helps companies generate value from artificial intelligence by knowing how to plan and use the right techniques to get to the finish line of a “long-distance” project. He believes that artificial intelligence will make our society better, and he is willing to do everything in his power to make this possible. He holds a Ph.D. in machine learning and physics and he is the founder of TOELT, a company focused on research in AI, and of the AI Center of Excellence at Helsana Versicherung AG in Switzerland. He is also a Google Developer Expert in Machine Learning based in Switzerland.
GDSC Munich Lead
GDSC Munich Lead
GDSC Munich Core Team
GDSC Munich Core Team
GDSC Munich Advisor
GDSC Munich Advisor
GDSC Munich Core Team
GDSC Munich Core Team
Technical University of Munich
GDSC Munich Core Member
GDSC Munich Core Member
TUM
GDSC Munich Core Team
GDSC Munich Core Team
