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Topic: 0 to 60 with Transfer (machine) Learning
There is an important element that drove the success that drove recent advances in AI and deep learning, Model building and time to market have been reduced greatly with the concept of transferring previous model's knowledge to a future model. With the transfer learning, you can get best of the both worlds. A state of the art model to start with and you can focus on customizing the model for your data. I will explain the basics of this concept with two applications (image classification and text topic modeling).
Presenter: Kiran Ramineni, Senior data scientist & Big data architect @ Intel
Kiran Ramineni earned his doctorate from University of California, Irvine in computer science in 2007. He worked in several corporations there after donning different roles ranging from software engineer, big data developer, architect to machine learning engineer. Currently, he is learning everyday and practicing data science at a major corporation in the Phoenix valley.
Location: Theatre Room - The University of Advancing Technology