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.
Past Activities
Elizabeth Ramirez
Code Mesh V
Code Mesh V
05 Nov 2020
16.15 - 16.55
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.
Elizabeth Ramirez
Code Mesh V
Code Mesh V
05 Nov 2020
14.40 - 15.10
14.40 - 15.10