Sören Dobberschütz · August 6, 2018

With this blog, we are going to explore the Tensorflow.jl-Package for the Julia Programming Language.

I am new to both Tensorflow and Julia - so please don’t expect any in-depth programming tricks or advanced API explanations. I will use Google’s newly released Machine Learning Crash Course and implement most of the exercises in a Jupyter notebook.

Why Julia?

Julia Logo

Julia is a rather new programming language, initially released in 2012. The things that attracted me personally are

  • Most of my professional life, I have developed code in Matlab. Julia’s syntax is very close to that of Matlab, which makes the transition very easy.
  • Julia is open-source. So no hassles with obtaining an expensive license.
  • Julia is fast (as compared to Matlab or Python). Its just-in-time compiler compiles the code to native machine code before execution. One of the first bits of code I wrote with Julia contained a part that enlarged and appended a big array for thousands of iterations. Of course this is very bad programming practise - but still it performed surprisingly well.

Why TensorFlow?

TF Logo

  • TensorFlow is open-source and used by Google and many other big companies for production machine learning tasks.


I am neither affiliated with Google (who runs the Machine Learning Crash Course), Julia or the creators of Tensorflow.jl. This is a pure just-for-fun hobby project.

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