Roland Schätzle – Medium

Roland Schätzle

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Roland Schätzle
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Towards Data Science

Python vs. Julia: It’s also about Consistency

The main advantages of Julia over Python are surely its speed and concepts like multiple dispatch. But there is more: In everyday use…

8 min readMay 19, 2022

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Python vs. Julia: It’s also about Consistency

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Roland Schätzle
in
Towards Data Science

Moving code from Python to Julia: Beware of false friends

In general it’s easy to transfer code from Python to Julia. But there are some pitfalls you should know.

10 min readMay 3, 2022

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Moving code from Python to Julia: Beware of false friends

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Roland Schätzle
in
Towards Data Science

Statistical Plotting with Julia: AlgebraOfGraphics.jl

How to create statistical plots using the AlgebraOfGraphics.jl (and Makie.jl) package

9 min readApr 19, 2023

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Statistical Plotting with Julia: AlgebraOfGraphics.jl

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Roland Schätzle
in
Towards Data Science

Statistical Plotting with Julia: VegaLite.jl

How to create statistical plots using the VegaLite.jl package

8 min readDec 22, 2022

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Statistical Plotting with Julia: VegaLite.jl

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Roland Schätzle
in
Towards Data Science

Statistical Plotting with Julia: Gadfly.jl

How to create statistical plots using the Gadfly.jl package

7 min readAug 25, 2022

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Statistical Plotting with Julia: Gadfly.jl

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Roland Schätzle
in
Towards Data Science

The Grammar of Graphics or how to do ggplot-style plotting in Julia

Introduction to a comparison of Julia graphics packages for statistical plotting

9 min readAug 12, 2022

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The Grammar of Graphics or how to do ggplot-style plotting in Julia

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Roland Schätzle
in
Towards Data Science

Flux.jl on MNIST — What about ADAM?

So far we’ve seen a performance analysis using the standard gradient descent optimizer. But which results do we get, if we use a more…

7 min readJul 11, 2022

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Flux.jl on MNIST — What about ADAM?

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Roland Schätzle
in
Towards Data Science

Flux.jl on MNIST — A performance analysis

After having defined a variety of models and training algorithms in a previous article, we have now a look on how well they perform when…

11 min readJun 30, 2022

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Flux.jl on MNIST — A performance analysis

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Roland Schätzle
in
Towards Data Science

Flux.jl on MNIST — Variations of a theme

Flux.jl is a ML-stack offering lightweight components to create models and train them. Using the MNIST dataset we will see how easy it is…

12 min readJun 23, 2022

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Flux.jl on MNIST — Variations of a theme

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Roland Schätzle
in
Towards Data Science

1:length(A) considered harmful — or how to make Julia code “generic safe”

Julia allows code to be written on a rather abstract and generic level, thus offering great expressive power. However, this advantage also…

7 min readJun 8, 2022

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1:length(A) considered harmful — or how to make Julia code “generic safe”

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Roland Schätzle

Roland Schätzle

CEO at adviion.de, Lecturer at KIT.edu and dhbw.de, Dr. rer. pol. —Fields of interest: Data Science, Machine Learning, Software Engineering, Project Management

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