Northwestern University 1 credit Mathematics
Neural networks and analysis of the approximation properties which underlie the mathematical principles of deep learning. Loss functions and training phenomena. Comparison of different activation functions and network architectures. Vapnik-Chervonenkis dimension and bit extraction. Prerequisites: MATH 320-1 or MATH 321-1.
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