You have performance problems with your Python code but are sick of rewriting it in Cython/C/C++? Julia might just be the language to solve your problems. It's ease of use rivals that of Python, but it runs as fast as C. It has a powerful, optional type system, which is great for writing high level and generic code. In this talk I will walk you over some examples that show off Julia as a great language for writing math and complex libraries. I will also explain, how one can wrap their Julia packages in Python, to also reach the people that are not yet ready to leave Python for a new, hip language.

Simon Danisch

Affiliation: Nextjournal

While studying Cognitive Science, Simon developed a great interest for Machine Learning and Computer Vision. During his one-year stay at the the Volkswagen Research lab in San Francisco, he was working on computer vision in C++. Looking for better alternatives to a cumbersome language like C++ or a slow language like Python got him interested in language design. This quickly led him to pick up Julia, where he supported work by the Julia MIT lab and authored a number of open source libraries for plotting, GPU acceleration and Machine Learning. Today, Simon is a researcher at Nextjournal, where he is responsible for making Julia easily accessible.

visit the speaker at: Twitter • Github • Homepage