Texas A&M University 3 credits MATH
Theory and practice of deep learning, including topics concerning approximation, generalization and optimization; study of the theory of universal approximation, stochastic gradient-based optimizers and statistical learning bounds, but also computational aspects including backpropagation and batch normalization. Prerequisite: MATH 304, MATH 251, MATH 411, and MATH 679 or equivalent; or approval of instructor.
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