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Elizabeth Ramirez

Working @ the intersection of High-Performance Computing, Numerical Linear Algebra, and Machine Learning.

Electrical Engineer and MSc. in Applied Mathematics with 14+ years of experience in Software Engineering and Data Science. Applied Scientist at Descartes Labs, focused in Maritime Intelligence models based in remote sensing. 

Upcoming Activities

Elizabeth Ramirez
Code Mesh V
05 Nov 2020
16.15 - 16.55

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.