• DARWIN Documentation

DARWIN (Delaware Advanced Research Workforce and Innovation Network) is a big data and high performance computing system designed to catalyze Delaware research and education

• Introduction to PyTorch for Deep Learning

PyTorch is a Python library that supports accelerated GPU processing for Machine Learning and Deep Learning. In this tutorial, I will teach the basics of PyTorch from scratch. I will then explore how to use it for some ML projects such as Neural Networks, Multi-layer perceptrons (MLPs), Sentiment analysis with RNN, and Image Classification with CNN.

• Leveraging AI in Generative Assets and Environments for Play

In this presentation, I will explore the recent advancements in AI-driven production of 3D-generative assets and environments, particularly focusing on their application in creating immersive, playful experiences. Platforms such as ChatGPT, Suno, and Speechify have ushered in a new era of digital creativity, facilitating the development of environments that not only entertain but also serve educational purposes. This session will delve into how these technologies are integrated into academic settings, specifically through a case study of the English Department's Digital Media Lab, known as Tech/Tech, which opened in 2022.

• ACESS HPC Workshop Series
• MPI Workshop
• OpenMP Workshop
• GPU Programming Using OpenACC
• Summer Boot Camp
• Big Data and Machine Learning

Monthly workshops sponsored by ACCESS on a variety of HPC topics organized by Pittsburgh Supercomputing Center (PSC). Each workshop will be telecast to multiple satellite sites and workshop materials are archived.

• Introduction to Python book

This documentation contains introductory material on Python Programming for Digital Humanities and Computational Research. This can be a go-to material for a beginner trying to learn Python programming and for anyone wanting a Python refresher.

• GPU Acceleration in Python

This tutorial explains how to use Python for GPU acceleration with libraries like CuPy, PyOpenCL, and PyCUDA. It shows how these libraries can speed up tasks like array operations and matrix multiplication by using the GPU. Examples include replacing NumPy with CuPy for large datasets and using PyOpenCL or PyCUDA for more control with custom GPU kernels. It focuses on practical steps to integrate GPU acceleration into Python programs.

• Python Tools for Data Science

Python has become a very popular programming language and software ecosystem for work in Data Science, integrating support for data access, data processing, modeling, machine learning, and visualization. In this webinar, we will describe some of the key Python packages that have been developed to support that work, and highlight some of their capabilities. This webinar will also serve as an introduction and overview of topics addressed in two Cornell Virtual Workshop tutorials, available at https://cvw.cac.cornell.edu/pydatasci1 and https://cvw.cac.cornell.edu/pydatasci2

• Pandas Docs

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. It lets you store data in easy to manage and display data frames, with column names and datatypes.

• Masters in Data Science Program Guide

A master’s degree in data science helps prepare professionals to take the next career step. This article will focus primarily on data science, a graduate degree in this field, and a data scientist or data analyst career. With many employers preferring a master’s degree in data science for those seeking to fill roles as data scientists or analysts, we will discuss the data science master’s degree in detail.

• OSF hosted knowledge base submission

It's not uncommon to see beautiful visualizations in HPC center galleries, but the majority of these are either rendered off the HPC or created using programs that run on OpenGL or custom rasterization techniques. To put it simply the next generation of graphics provided by OpenGL's successor Vulkan is strangely absent in the super computing world. The aim of this survey of available resources is to determine the systems that can support Vulkan workflows and programs. This will assist users in getting past some of the first hurdles in using Vulkan in HPC contexts.

• Scikit-learn

Scikit-learn is free software machine learning library for Python. It has a variety of features you can use on data, from linear regression classifiers to xg-boost and random forests. It is very useful when you want to analyze small parts of data quickly.

• Big Data Research at the University of Colorado Boulder

Background: Big data, defined as having high volume, complexity or velocity, have the potential to greatly accelerate research discovery. Such data can be challenging to work with and require research support and training to address technical and ethical challenges surrounding big data collection, analysis, and publication. Methods: The present study was conducted via a series of semi-structured interviews to assess big data methodologies employed by CU Boulder researchers across a broad sample of disciplines, with the goal of illuminating how they conduct their research; identifying challenges and needs; and providing recommendations for addressing them. Findings: Key results and conclusions from the study indicate: gaps in awareness of existing big data services provided by CU Boulder; open questions surrounding big data ethics, security and privacy issues; a need for clarity on how to attribute credit for big data research; and a preference for a variety of training options to support big data research.

• Numpy and Pandas for Data Analysis

In this tutorial, I present an overview with many examples of the use of Numpy and Pandas for data analysis. Beginners in the field of data analysis can find It incredibly helpful, and at the same time, anyone who already has experience in data analysis and needs a refresher can find value in it. I discuss the use of Numpy for analyzing 1D and 2D multidimensional data and an introduction on using Pandas to manipulate CSV files.

• The Ultimate Guide to LLM Fine Tuning: Best Practices & Tools

With the recent uprising of LLM's many business are looking at way to adopt these LLMs and fine-tuning these models on specfic data sets to ensure accuracy. These models when fine-tuned can be optimal for fulfilling the specific needs of a company. This site explains explicitly when, how, and why models should be trained. It goes over various strategies for LLM fine -tuning.

• marimo | a next generation python notebook

Introduction seminar for new reactive python notebook from marimo ambassador.

• NumPY Docs

Numpy is a python package that leverages types and compiled C code to make many math operations in Python efficient. It is especially useful for matrix manipulation and operations.

• Expanse Home Page

Expanse at SDSC is a cluster designed by Dell and SDSC delivering 5.16 peak petaflops, and offers Composable Systems and Cloud Bursting.

• Weka Homepage
• Weka Data Mining Tutorials

Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.

• Online Data Science Degree Programs

The realm of data science is one that onlookers regard with curiosity and respect. There are a lot of unknowns in this area of study that only recently became hugely relevant. It is important to get the facts on how expertise in data science is transforming the world. This article features what a bachelor’s degree means in today’s market and the future.

• scikit learn tutorial

In the realm of Python-based machine learning, Scikit-Learn stands out as one of the most powerful and versatile tools available. This introductory post serves as a gateway to understanding Scikit-Learn through explanations of introductory ML concepts along with implementations examples in Python.

• Spatial Data Science in the Cloud (Alpine HPC) using Python Webpage

Spatial Data Science is a growing field across a wide range of industries and disciplines. The open-source programming language Python has many libraries that support spatial analysis, but what do you do when your computer is unable to tackle the massive file sizes of high-resolution data and the computing power required in your analysis? There materials have been prepared to teach you spatial data science and how to execute your analysis using a high-performance computer (HPC).

• phenoACCESS-24: Workshop on Research Computing and Plant Phenotyping

phenoACCESS-24: Workshop on Research Computing and Plant Phenotyping High-throughput plant phenotyping is computationally intensive, requiring data storage, data processing and analysis, research computing expertise, and mechanisms for data sharing. This workshop is aimed at research computing workforce development by addressing questions such as what is plant phenotyping; what types of data are collected; what are the preprocessing and analytical needs; what tools and platforms exist for data capture, management, analysis, and storage; and how best to collaborate and engage with phenotyping researchers. The full-day agenda will include speakers (scientists and research compute staff); panel discussions (how to work with research computing staff and facilities; how to engage with phenotyping scientists), and networking opportunities (meet-and-greet, ice breakers, small group discussions). The videos and slide decks for the talks are included on the linked page.

• Displaying Scientific Data with Tableau

Tableau is a popular and capable software product for creating charts that present data and dashboards that allow you to explore data. It is typically used to present business or statistical data, but can also create compelling visualizations of scientific data. However, scientific data is often generated or stored in formats that are not immediately accessible by Tableau. This seminar will explore the data formats that work best with Tableau and the available mechanisms for generating scientific data in (or converting it to) those formats so that you can apply the full power of Tableau to create the best possible visualizations of your data.

• Beautiful Soup Docs

This package lets you easily scrape websites and extract information based on html tags and various other metadata found in the page. It can be useful for large-scale web analysis and other tasks requiring automated data gathering.

• Find Online Master's in Business Analytics

A degree in business analytics looks different in today’s world than it did a decade ago. In its most current application, business analytics uses modern data science and capabilities in machine learning (ML). The magic comes into play when these are leveraged for strategic planning.