Julia is a high-level, high-performance dynamic language for scientific computing. It has been gaining traction as a faster alternative to Matlab, R and NumPy and as a more productive alternative to C, C++ and Fortran. Julia is particularly relevant when both expressiveness and performance are paramount – in areas like machine learning, “big statistics”, linear algebra, bioinformatics, and image analysis.
Showing posts with label performance. Show all posts
Showing posts with label performance. Show all posts
Thursday, February 12, 2015
Subscribe to:
Posts (Atom)
Popular Articles
-
Two great things about Ruby are it's brevity and it's dynamic nature. A great way to introduce Ruby to a project written in another...
-
At April's Tech Talk Tuesday , we previewed six Modern Customer Relationship Management (CRM) Systems . There are hundreds of CRM ...
-
Cloud Computing has graduated from being a technology buzzword to being an essential part of any business or organization. Let's take a... -
At the last Tech Talk Tuesday we took an overview of Python's Data Science related packages. The key packages for numerical...
