Deep learning, concepts and frameworks: Find your way through the jungle (talk)

Today at OOP in Munich, I had an in-depth talk on deep learning, including applications, basic concepts as well as practical demos with Tensorflow, Keras and PyTorch.

As usual, the slides are on RPubs, split up into 2 parts because of the plenty of images included – lossy png compression did work wonders but there’s only so much you can expect 😉 – so there’s a part 1 and a part 2.

There’s also the github repository with the demo notebooks.

Thanks to everyone who attended, and thank you for the interesting questions!
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I’m a developer, why should I care about matrices or calculus? (talk at MLConference 2017)

Yesterday at ML Conference, which took place this year for the first time, I had a talk on cool bits of calculus and linear algebra that are useful and fun to know if you’re writing code for deep learning and/or machine learning.

Originally, the title was something like “What every interested ML/DL developer should know about matrices and calculus”, but then really I didn’t like the schoolmasterly tone that had, as really what I’ve wanted to convey was the fun and the fascination of it …

So, without further ado, here are the slides and the raw presentation on github.

Thanks for reading!