top of page

Say Hello to “Julia”

We are all overwhelmed by the programming /scripting language options we have for implementing AI/DL/ML. In this blog post, I am covering the landscape of available options and in specific giving away some beginners dope on Julia.

There are several open source and commercial Machine learning frameworks and tools in the market that have evolved over the last few decades. While the field of Machine learning itself is evolving in building powerful algorithms for diverse requirements across domains, we now see a surge of open source options for large-scale Machine learning that have reached a significant level of maturity and are being widely adopted by the data science and Machine learning communities. [embed]https://www.datadriveninvestor.com/2019/02/07/8-skills-you-need-to-become-a-data-scientist/[/embed]

The model has changed significantly in the recent past, and the researchers are encouraged to publish their software under an open source model. Since there are problems that authors face while publishing their work in using algorithmic implementations for Machine learning, any work that is reviewed and improvised through usage by the data science community is considered to be of more value.

The following concept map depicts some important commercial and open source Machine learning frameworks and tools in the market.

Some of these libraries are around specific programming languages such as Java, Python, C++, Scala, and so on. Some of these libraries like Julia, Spark, and Mahout already support distributed, and parallel processing and others such as R and Python can run as MapReduce functions on Hadoop.

Julia

Julia, in the recent times,