Fluid: Towards Transparent, Self-Explanatory Research Outputs
Offered By: ACM SIGPLAN via YouTube
Course Description
Overview
Explore a 19-minute conference talk introducing Fluid, a "transparent" programming language that incorporates bidirectional dynamic dependency analysis into its runtime. Learn how Fluid tracks dependencies as outputs like charts and tables are computed from data, automatically enriching rendered outputs with interactive elements. Discover how this innovative approach allows readers to explore the relationship between inputs and outputs, promoting transparent and self-explanatory research outputs. Gain insights from speakers Joe Bond, Cristina David, Minh Nguyen, and Roly Perera as they present this cutting-edge development in programming language design at the ACM SIGPLAN conference.
Syllabus
[PROPL'24] Fluid: towards transparent, self-explanatory research outputs
Taught by
ACM SIGPLAN
Related Courses
Machine Learning With Big DataUniversity of California, San Diego via Coursera Recolección y exploración de datos
Tecnológico de Monterrey via Coursera Capstone: Create Value from Open Data
ESSEC Business School via Coursera Big Data - Capstone Project
University of California, San Diego via Coursera Análisis de Datos - Proyecto Final
Tecnológico de Monterrey via Coursera