07-28, 14:00–17:30 (UTC), Red Track
MLJ is a machine learning framework for Julia aiming to provide a convenient way to use and combine a multitude of tools and models available in the Julia ML/Stats ecosystem.
Join via Zoom: link in email from Eventbrite. Backup Youtube link: https://youtu.be/qSWbCn170HU
In this workshop we intend to demonstrate how MLJ can be used on a few "real" examples demonstrating how to interpret and preprocess data, how to train models and tune hyperparameters and how to evaluate and compare the quality of predictive models.
We will also discuss briefly how MLJ positions itself with respect to the Julia ML/Stats ecosystem and what's on the roadmap for future developments.
Workshop repo: https://github.com/ablaom/MachineLearningInJulia2020
Dr. Anthony Blaom conducted his PhD at Caltech in Mathematics and has expertise in Differential Geometry, Dynamical Systems, Fluid Mechanics, Machine Learning and Applied Statistics. An erstwhile academic, Anthony is now primarily involved in software development for machine learning.
TBD