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Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA
Danielle Dean

Danielle Dean
Principal Data Scientist Lead, Microsoft

Website | @danielleodean

Danielle Dean is a principal data scientist lead in AzureCAT within the Cloud AI Platform Division at Microsoft, where she leads an international team of data scientists and engineers to build predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. Danielle holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multilevel event history models to understand the timing and processes leading to events between dyads within social networks.


11:05am-11:45am Thursday, September 6, 2018
Models and Methods
Location: Yosemite BC
Secondary topics:  Computer Vision, Deep Learning models
Wee Hyong Tok (Microsoft), Danielle Dean (Microsoft)
Average rating: ***..
(3.33, 3 ratings)
Transfer learning enables you to use pretrained deep neural networks and adapt them for various deep learning tasks (e.g., image classification, question answering, and more). Join Wee Hyong Tok and Danielle Dean to learn the secrets of transfer learning and discover how to customize these pretrained models for your own use cases. Read more.