The Linear Algebra of Deep Learning

Data Science relies heavily on mathematical tools: linear algebra, optimization, probability and statistics. We aim to present Linear Algebra fundamental primitives for Deep Learning, which make computations especially fast.

OBJECTIVES

Understanding the mathematical background and algorithms behind the most common Deep Learning primitives and operations, such as GEMM, Autodiff, and convolution.

AUDIENCE

Data Scientists and Engineers, HPC Engineers. 

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Start booking your calendar with more Code Sync conferences happening across the globe. We will be slowly releasing more dates, in the meantime here is what we’ve planned already:

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