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Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY
Shaked Shammah

Shaked Shammah
Graduate Student, Hebrew University

Shaked Shammah is a graduate student at the Hebrew University, where he works under Shai Shalev-Shwartz, and a researcher at Mobileye Research. Shaked’s work focuses on general machine learning and optimization, specifically the theory and practice of deep learning and reinforcement learning.

Sessions

Failures of gradient-based deep learning
Artificial Intelligence
 Location: 1A 06/07 Level: Intermediate
Secondary topics:  Deep learning
Shaked Shammah (Hebrew University)
Average rating: ****.
(4.00, 1 rating)
Deep learning is amazing, but it sometimes fails miserably, even for very simple, practical problems. Shaked Shammah discusses four types of common problems in which deep learning fails. Some can be solved by using specific approaches to network architecture and loss functions. For others, deep learning is simply not the right way to go. Read more.