• Active inference institute website

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.

• Why Mentoring Matters and How to Get Started

Describes effective mentorship (both ways).

• Anvil Home Page

Purdue University is the home of Anvil, a powerful supercomputer that provides advanced computing capabilities to support a wide range of computational and data-intensive research spanning from traditional high-performance computing to modern artificial intelligence applications.

• Discover Data Science

Discover Data Science is all about making connections between prospective students and educational opportunities in an exciting new, hot, and growing field – data science.

• RMACC.org

Rocky Mountain Advanced Computing Consortium Website

• unix list of basic commands

Unix is incredibly common and useful. This website provides all the common commands and explanations for one to get started with a unix system.

• RRCoP External resources Page

Very helpful list of Regulated Research Community of Practice's collaborating communities.

• Application Fundamentals

The provided text discusses various aspects of Android app development fundamentals. It covers key concepts related to app components, the AndroidManifest.xml file, and app resources. Android apps are built using various components, including Activities, Services, Broadcast Receivers, and Content Providers. These components serve different purposes and have distinct lifecycles. Activities are used for user interaction, services for background tasks, broadcast receivers for system-wide event handling, and content providers for managing shared data.The AndroidManifest.xml file is essential for declaring app components, permissions, and other settings. It informs the Android system about the app's components and capabilities. For instance, it specifies the minimum API level, declares hardware and software requirements, and defines intent filters to enable components to respond to specific actions.It's crucial to declare app requirements, such as device features and minimum Android API levels, to ensure compatibility with different devices and configurations. These declarations help in filtering the app's availability on Google Play for users with compatible devices.Android apps rely on resources separate from code, including images, layouts, strings, and more. These resources are stored in various directories and can be tailored for different device configurations. Providing alternative resources allows for optimization across different languages, screen sizes, orientations, and other factors. Understanding these fundamentals is essential for developing Android applications effectively, ensuring compatibility, and providing a consistent user experience across a wide range of devices and configurations.

• Cybersecurity Guide

Cybersecurity Guide is a comprehensive resource for students and early career professionals that provides users with a wide range of resources and up-to-date information on cybersecurity, including cybersecurity degree programs and bootcamps, career guides, as well as online courses and training opportunities. Additionally, it covers trends, best practices, and much more.

• NITRC

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.

• Navier-Stokes Cahn-Hilliard (NSCH) for MOOSE Framework

The MOOSE Navier-Stokes Cahn-Hilliard (NSCH) application is a library for implementing simulation tools that solve the Navier-Stokes Cahn-Hilliard equations with non-matching densities using Galerkin finite element methods with a residual-based stabilization scheme.

• Feed-Forward and SGD

Feed-forward neural networks are a simple type of network that simply rely on data to be "fed-forward" through a series of layers that makes decisions on how to categorize datum. Gradient descent is a type of optimization tool that is often used to train machines. These two areas in ML are good starting points and are the easiest types of neural network/optimization to understand.

• InsideHPC HomePage

InsideHPC is an informational site offers videos, research papers, articles, and other resources focused on machine learning and quantum computing among other topics within high performance computing.