Installing Rocky Linux Operating System
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Rocky Linux is an open-source enterprise operating system. It is compatible with Red Hat Enterprise Linux (RHEL). It is a community-driven project that provides a stable and reliable platform for production workloads. It is one of the best alternatives to Opensource CentOS, since Centos will be on end of life (EoL) soon in 2024 by shifting to CentOS Stream.
Rockfish at Johns Hopkins University
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Resources and User Guide available at Rockfish
QGIS Processing Executor
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Running QGIS tools from the command line
Texas A&M HPRC Training Site
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Training Resources and Courses offered by Texas A&M's Research Computing Group
Data Imputation Methods for Climate Data and Mortality Data
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This slices and videos introduced how to use K-Nearest-Neighbors method to impute climate data and how to use Bayesian Spatio-Temporal models in R-INLA to impute mortality data. The demos will be added soon.
Resource to active inference
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Active inference is an emerging study field in machine learning and computational neuroscience. This website in particular introduces "active inference institute", which has established a couple of years ago, and contains a wide variety of resources for understanding the theory of active inference and for participating a worldwide active inference community.
Building Anaconda Navigator applications
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This tutorial explains how to create an Anaconda Navigator Application (app) for JupyterLab. It is intended for users of Windows, macOS, and Linux who want to generate an Anaconda Navigator app conda package from a given recipe. Prior knowledge of conda-build or conda recipes is recommended.
MDAnalysis - Python library for the analysis of molecular dynamics simulations
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MDAnalysis is a python based library of tools for the analysis of molecular dynamics simulations. It is able to read and write many popular simulation formats including CHARMM, LAMMPS, GROMACS, and AMBER and more. This link contains the documentation pages of all MDAnalysis functions and has links to tutorials using Jupyter Notebooks.
Numba: Compiler for Python
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Numba is a Python compiler designed for accelerating numerical and array operations, enabling users to enhance their application's performance by writing high-performance functions in Python itself. It utilizes LLVM to transform pure Python code into optimized machine code, achieving speeds comparable to languages like C, C++, and Fortran. Noteworthy features include dynamic code generation during import or runtime, support for both CPU and GPU hardware, and seamless integration with the Python scientific software ecosystem, particularly Numpy.
ACCESS Getting Started Quick-Guide
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A step-by-step guide to getting your first allocation for Access computing and storage resources.
Weka
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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.
Trusted CI Resources Page
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Very helpful list of external resources from Trusted CI
What is fairness in ML?
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This article discusses the importance of fairness in machine learning and provides insights into how Google approaches fairness in their ML models.
The article covers several key topics:
Introduction to fairness in ML: It provides an overview of why fairness is essential in machine learning systems, the potential biases that can arise, and the impact of biased models on different communities.
Defining fairness: The article discusses various definitions of fairness, including individual fairness, group fairness, and disparate impact. It explains the challenges in achieving fairness due to trade-offs and the need for thoughtful considerations.
Addressing bias in training data: It explores how biases can be present in training data and offers strategies to identify and mitigate these biases. Techniques like data preprocessing, data augmentation, and synthetic data generation are discussed.
Fairness in ML algorithms: The article examines the potential biases that can arise from different machine learning algorithms, such as classification and recommendation systems. It highlights the importance of evaluating and monitoring models for fairness throughout their lifecycle.
Fairness tools and resources: It showcases various tools and resources available to practitioners and developers to help measure, understand, and mitigate bias in machine learning models. Google's TensorFlow Extended (TFX) and What-If Tool are mentioned as examples.
Google's approach to fairness: The article highlights Google's commitment to fairness and the steps they take to address fairness challenges in their ML models. It mentions the use of fairness indicators, ongoing research, and partnerships to advance fairness in AI.
Overall, the article provides a comprehensive overview of fairness in machine learning and offers insights into Google's approach to building fair ML models.
Time-Series LSTMs Python Walkthrough
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A walkthrough (with a Google Colab link) on how to implement your own LSTM to observe time-dependent behavior.
NITRC
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The Neuroimaging Tools and Resources Collaboratory (NITRC) is a neuroimaging informatics knowledge environment for MR, PET/SPECT, CT, EEG/MEG, optical imaging, clinical neuroinformatics, imaging genomics, and computational neuroscience tools and resources.
Developer Stories Podcast
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As developers, we get excited to think about challenging problems. When you ask us what we are working on, our eyes light up like children in a candy store. So why is it that so many of our developer and software origin stories are not told? How did we get to where we are today, and what did we learn along the way? This podcast aims to look “Behind the Scenes of Tech’s Passion Projects and People.” We want to know your developer story, what you have built, and why. We are an inclusive community - whatever kind of institution or country you hail from, if you are passionate about software and technology you are welcome!
ACCESS KB Guide - Expanse
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Expanse at SDSC is a cluster designed by Dell and SDSC delivering 5.16 peak petaflops, and offers Composable Systems and Cloud Bursting.
MOPAC
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MOPAC (Molecular Orbital PACkage) is a semi-empirical quantum chemistry package used to compute molecular properties and structures by using approximations of the Schrödinger equation. This tutorial explains the process of using MOPAC for different forms of calculations.
UNIX/command line basics tutorial
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Introductory training materials for working on the UNIX command line.
Mechanism and Implementation of Various MPI Libraries
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There is a detailed explanation about communication routines and managing methods of different MPI libraries, as well as several exercises designed for users to get familiar with the implementation of MPI build process.
Solving differential equations with Physics-informed Neural Network
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Differential equations, the backbone of countless physical phenomena, have traditionally been solved using numerical methods or analytical techniques. However, the advent of deep learning introduces an intriguing alternative: Physics-Informed Neural Networks (PINNs). By leveraging the representational power of neural networks and integrating physical laws (like differential equations), PINNs offer a novel approach to solving complex problems. This guide walks through an implementation of a PINN to solve DEs such as the logistic equation.