Research Computing Workshop

  • Fundamentals of Deep Learning (NVIDIA), Research Computing Bootcamp

    Fri, Jan 29, 2021, 9:00 am
    Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software.
  • GPU HPC Bootcamp (NVIDIA), Research Computing Bootcamp

    Thu, Jan 28, 2021, 9:00 am

    Full event details and registration link here.

    This day-long workshop will teach participants the basics of GPU programming through extensive hands-on collaboration based on real-life codes using the OpenACC programming model.

  • Data Visualization in R, using ggplot2 with Daisy Huang, Research Computing Bootcamp

    Thu, Jan 21, 2021, 9:30 am
    This workshop provides an introduction to effective data visualization in R, primarily using the graphics package ggplot2. We will discuss main concepts of the grammar that defines the graphical building blocks of that package, and we will use hands-on examples to explore ggplot2’s layered approach to creating basic and more complex graphs. Participants should have at least basic experience with R and feel comfortable working with R data frames, but those relatively new to R may still find value in the workshop and are welcome to attend.
  • Intro to Data Analysis Using R w/ Brian Arnold & Andrzej Zuranski (Schmidt DataX), Research Computing Bootcamp

    Wed, Jan 20, 2021, 9:00 am
    This session in an introduction to data analysis using the R programming language, aimed at people who have ever used R or RStudio before. It will briefly cover different facets of data analysis and their execution using basic R. The style is fairly hands-on, with participants executing the examples on their own laptops alongside the instructors. Topics covered include: vectors, vector math, and subsetting vectors; object types; logical vectors; reading/writing files; the basics of data frames; how to compute basic summary statistics (e.g. mean, min, max, sd); basic R functions for plotting (plot, hist, etc); and how to install additional R packages that extend R’s native functionality.
  • Introduction to NumPy with Vineet Bansal, Research Computing Bootcamp

    Wed, Jan 20, 2021, 1:30 pm
    This session covers the basics of NumPy, the package that underlies most scientific computing done in Python. It will explain the NumPy array, the principal data type in the NumPy package, and how it differs from similar Python structures like lists. There will be particular emphasis on understanding the two core features of NumPy arrays – vectorization and broadcasting – and how they can be leveraged to write concise and powerful scientific code in Python. There will be hands-on exercises, including with multidimensional arrays.
  • Introduction to the Machine Learning Libraries

    Wed, Oct 28, 2020, 4:30 pm

    The Princeton HPC clusters offer several machine learning (ML) software libraries. Some are straightforward to use while others need to be installed and are highly configurable. Additional complications arise when job scheduler scripts need to be written to take advantage of multi-threading and/or GPUs. This workshop will show participants how to get started with various ML libraries and use these libraries optimally on the HPC clusters. We will cover PyTorch, TensorFlow, Spark, NVIDIA Rapids, R, Julia and more.

  • Intro to Data Analysis using Python

    Wed, Oct 28, 2020, 10:00 am

    This workshop will get students started in data analysis using the pandas Python package. It will briefly cover different components of data analysis and connect them with the goal of extracting meaning from data. We will go over an example to illustrate the data analysis process from beginning to end. This workshop is ideal for those who are at the initial stages of doing independent research requiring quantitative analysis (term paper, dissertation, junior paper, senior thesis).

  • Machine Learning for Your Research

    Tue, Oct 20, 2020, 4:30 pm

    This workshop will give an overview of several modern supervised and unsupervised machine learning methods. We will discuss the advantages and limitations of each and explore what types of problems each is best suited to address.

    Workshop format: Lecture and discussion

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