This video was recorded at Code Mesh V 2020 - https://codesync.global/conferences/code-mesh-ldn/ The Linear Algebra of Deep Learning | Elizabeth Ramirez - Software Engineer ABSTRACT 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. • Follow us on social: Website: https://codesync.global/conferences/code-mesh-ldn Twitter: https://twitter.com/CodeMeshIO • Looking for a unique learning experience? Attend the next Code Sync conference near you! See what's coming up at: https://codesync.global • SUBSCRIBE TO OUR CHANNEL https://www.youtube.com/channel/UC47eUBNO8KBH_V8AfowOWOw