Fall in love with Julia: Scientific Machine Learning (SciML) in Julia 101

Hi all, this introductory series is for you:

• you do Data Science in R, Python, Matlab, Fortran, etc.?
• you like it high-level and high-performant?

Machine Learning is about learning from data, Science is about modelling reality. Scientific Machine Learning combines both worlds: Build physics informed ml models, learn time series as dynamically systems, ...

We are going to take tour through Julia's SciML ecosystem. You can seamlessly combine Neural Networks (Flux.jl) with DifferentialEquations.jl (DiffEqFlux.jl) and probabilistic modelling (Turing.jl).

As usual in this series, you will get a beginner-friendly hands-on experience using a Jupyter-Notebook, with which you can try out SciML yourself and also experiment further afterwards. The material will be made available at the github repository https://github.com/schlichtanders/fall-in-love-with-julia
You do not need to prepare anything.

Looking forward to see you all!
Spread the news and invite everyone to this Julia 101!
It is going to be online, so everyone from everywhere is welcome.

yours,
Stephan Sahm
Founder of Jolin.io consultancy
www.jolin.io

P.S.: The link to the online tool will be made available 10 min before the actual start. Please join 5 min ahead, the session is going to start on-time, 18:30 CET.