2020-12-02, 16:05–16:20, Zoom Breakout Room 1
Julia is an open source, high-performance programming language for numerical and scientific computing. Its launch billed the language as having “the speed of C with the usability of Python, the dynamism of Ruby, the mathematical prowess of MatLab, and the statistical chops of R”. Julia aims to solve the “two-language” problem and as it was designed from the beginning for high performance and with technical and numerical computing usage in mind it is perfectly suited to a range of data science applications.
Julia is very much in its infancy compared with R and Python, launching in 2012 with a stable 1.0 release in 2018. Julia provides support for modern machine learning frameworks such as TensorFlow and MXNet, making it easy to adapt to existing workflows, and supports a number of statistical and data science applications built in R and Python. While the Julia community is growing, many frameworks are still in the process of being ported natively into Julia.
This presentation provides a brief introduction to the Julia programming language and explores some deep learning implementations including some examples using Flux.