Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference
Singapore
Danielle Dean

Danielle Dean
Principal Data Scientist Lead, Microsoft

Website | @danielleodean

Danielle Dean is a principal data scientist lead at Microsoft in the Algorithms and Data Science Group within the Artificial Intelligence and Research Division, where she leads a team of data scientists and engineers building 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.

Sessions

12:05pm12:45pm Wednesday, December 6, 2017
Data science and advanced analytics, Machine Learning
Location: Summit 2 Level: Intermediate
Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
Average rating: ***..
(3.25, 4 ratings)
Transfer learning enables you to use pretrained deep neural networks (e.g., AlexNet, ResNet, and Inception V3) and adapt them for custom image classification tasks. Danielle Dean and Wee Hyong Tok walk you through the basics of transfer learning and demonstrate how you can use the technique to bootstrap the building of custom image classifiers. Read more.
11:15am11:55am Thursday, December 7, 2017
Big data and the cloud, Machine Learning
Location: Summit 2 Level: Intermediate
Wee Hyong Tok (Microsoft), Danielle Dean (Microsoft)
Deep neural networks are responsible for many advances in natural language processing, computer vision, speech recognition, and forecasting. Danielle Dean and Wee Hyong Tok illustrate how cloud computing has been leveraged for exploration, programmatic training, real-time scoring, and batch scoring of deep learning models for projects in healthcare, manufacturing, and utilities. Read more.