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Kuang Hao, Research Computing, NUS Information Technology, on 21 January 2021

Natural Language Processing (NLP) is getting more and more popular. Captivating as it is, there is quite a steep learning curve for NLP, as it stretches across multiple research areas, from computational linguistics to artificial intelligence. For beginners, apart from NLTK, the powerful NLP toolkit I introduced last time, there is another extremely user-friendly tool: TextBlob. In this article, we will explore what TextBlob can do to make your NLP learning easier.

Ku Wee Kiat, Research Computing, NUS Information Technology, on 21 January 2021

This series aims to provide an introduction to Julia for Data Science.
For this article, we will cover the benefits of Julia, some resources to look into for performing machine learning and other related tasks with Julia.

Tan Chee Chiang, Research Computing, NUS Information Technology, on 21 January 2021

The last issue I shared about greater enterprise adoption of HPC resources and technologies due to the emergence of AI as a common application. This round we will look into how the NUS IT Research Computing (REC) team has evolved to stay relevant in this new HPC-AI era.

by Wang Junhong, Research Computing, NUS Information Technology , on 24 September 2020

Each month an intuitive usage profiling of HPC resources will be generated for every HPC user. This will improve user experience and allow users to understand how well their jobs are performing in the aspect of number of jobs completed, waiting time vs running time, parallel speedup performance, efficiency, and memory usage. Such usage profiling data will not only help users to identify room for improvement in either the parallel performance or the memory utilisation of their HPC jobs, but also improve the overall HPC resources utilisation and planning for future expansion. Read on for more details. 

by Vamshidhar Gangu, Research Computing, NUS Information Technology, on 24 September 2020

One of the major complaints within R community is that it is very hard to maintain project level package dependencies as R is continuously evolving with several releases within a year.

The renv package is a new effort to bring project-local R dependency management to your projects. This is a robust, stable replacement for the Packrat package, with fewer surprises and better default behaviours.