Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Yahav Shadmi
Data Scientist , Intel

Yahav Shadmi is a senior data scientist in Intel’s Advanced Analytics department, a group that provides solutions for diverse company challenges using machine learning and big data techniques, where he leads data science projects and solves data-driven problems in the CPU design and architecture domain. Yahav’s current research is on optimization acceleration of deep learning tasks. He holds an MSc in computer science and machine learning from the Haifa University, Israel.

Sessions

12:00–12:30 Tuesday, 23 May 2017
Reducing neural-network training time through hyperparameter optimization
Data science and advanced analytics
 Location: London Suite 2/3
Secondary topics:  AI, Deep learning
Level: Intermediate
Amitai Armon (Intel), Yahav Shadmi (Intel)
Average rating: *****
(5.00, 2 ratings)
Neural-network models have a set of configuration hyperparameters tuned to optimize a given model's accuracy. Yahav Shadmi demonstrates how to select hyperparameters to significantly reduce training time while maintaining accuracy, present examples for popular neural network models used for text and images, and describe a real-world optimization method for tuning. Read more.