Neural Networks in Julia
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Making a neural network has never been easier! The following link directs users to the Flux.jl package, the easiest way of programming a neural network using the Julia programming language. Julia is the fastest growing software language for AI/ML and this package provides a faster alternative to Python's TensorFlow and PyTorch with a 100% Julia native programming and GPU support.
Bridges-2 Home Page
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Landing Page for Bridges-2 information
CaRCC Data Facing Track
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The Data-Facing Track of the People Network brings together people from research computing groups, libraries, research institutes, and other organizations who support data-enabled research. Many of us are also Researcher-Facing, but this track is an opportunity to discuss the varied challenges of working with data.
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.
TensorFlow for Deep Neural Networks
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TensorFlow is a powerful framework for Deep Learning, developed by google. This specifically is their python package, which is easy to use and can be used to train incredibly powerful models.
Long Tales of Science: A podcast about women in HPC
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A series of interviews with women in the HPC community
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.
Termius - Modern ssh platform
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**Termius: The Modern SSH Client for 2023**
Termius is the future-facing SSH client that's redefining remote server access in 2023. Designed for ease and efficiency, Termius offers a seamless connection experience across all devices, be it mobile or desktop. Gone are the days of re-inputting IP addresses, ports, and passwords; with Termius, one-click connectivity is the new norm.
**How Termius Elevates Remote Server Access:**
1. **One-Click Connectivity:** Save the hassle of remembering and re-entering connection details. Termius provides an immediate connection to your infrastructure with a single click.
2. **Synchronized Across Devices:** Termius ensures that your data, connection settings, and preferences are consistent across all your devices, from mobile to desktop.
3. **Unparalleled Security:** With the Cloud Vault feature, users can securely store their data in an encrypted environment, accessible only from their specific devices. Shared vaults allow for safe connection sharing within teams.
4. **AI-Powered Terminal Experience:** Advanced AI-driven autocomplete means users can input command descriptions, and Termius will swiftly convert them into accurate bash commands, simplifying and enhancing the terminal interaction.
5. **Collaborative Troubleshooting:** Share terminal sessions with teammates, facilitating cooperative problem-solving or knowledge sharing. No additional server-side installations needed.
6. **Automation and Snippets:** Streamline routine processes with the ability to save and run frequently used shell scripts. Sharing these Snippets with your team can lead to increased productivity and fewer manual errors.
7. **All-Device Compatibility:** Whether on iPad, iPhone, Android, macOS, Windows, or Linux, Termius ensures a consistent and fluid experience. The platform's synchronization capability means you're always ready to respond swiftly, irrespective of the device in use.
For professionals and businesses aiming for top-notch server access efficiency, Termius is the gold standard in 2023. Experience the revolution in SSH connectivity and optimize your workflow with Termius.
RMACC Website
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Rocky Mountain Advanced Computing Consortium Website
DAGMan for orchestrating complex workflows on HTC resources (High Throughput Computing)
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DAGMan (Directed Acyclic Graph Manager) is a meta-scheduler for HTCondor. It manages dependencies between jobs at a higher level than the HTCondor Scheduler.
It is a workflow management system developed by the High-Throughput Computing (HTC) community, specifically for managing large-scale scientific computations and data analysis tasks. It enables users to define complex workflows as directed acyclic graphs (DAGs). In a DAG, nodes represent individual computational tasks, and the directed edges represent dependencies between the tasks. DAGMan manages the execution of these tasks and ensures that they are executed in the correct order based on their dependencies.
The primary purpose of DAGMan is to simplify the management of large-scale computations that consist of numerous interdependent tasks. By defining the dependencies between tasks in a DAG, users can easily express the order of execution and allow DAGMan to handle the scheduling and coordination of the tasks. This simplifies the development and execution of complex scientific workflows, making it easier to manage and track the progress of computations.
RRCoP Resources Page
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Very helpful list of Regulated Research Community of Practice's collaborating communities.
Recommended Libraries for Cyberinfrastructure Users Developing Jupyter Notebooks
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This repository contains information about Jupyter Widgets and how they can be used to develop interactive workflows, data dashboards, and web applications that can be run on HPC systems and science gateways. Easy to build web applications are not only useful for scientists. They can also be used by software engineers and system admins who want to quickly create tools tools for file management and more!
OnShape FeatureScripts: Custom features for everyone
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OnShape FeatureScripts allow users to create their own features via OnShape's programming language. The user can make these as simple or complex as they need, and they can save tons of time for heavy OnShape users or complex projects!
PyTorch Introduction
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This is a very barebones introduction to the PyTorch framework used to implement machine learning. This tutorial implements a feed-forward neural network and is taught completely asynchronously through Stanford University. A good start after learning the theory behind feed-forward neural networks.
Implementing Markov Processes with Julia
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The following link provides an easy method of implementing Markov Decision Processes (MDP) in the Julia computing language. MDPs are a class of algorithms designed to handle stochastic situations where the actor has some level of control. For example, used at a low level, MDPs can be used to control an inverted pendulum, but applied in higher level decision making the can also decide when to take evasive action in air traffic management. MDPs can also be extended to the partially observable domain to form the Partially Observable Markov Decision Process (POMDP). This link contains a wealth of information to show one can easily implement basic POMDP and MDP algorithms and apply well known online and offline solvers.
ACCESS Support Portal
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Open Storage Network
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The Open Storage Network, a national resource available through the XSEDE resource allocation system, is high quality, sustainable, distributed storage cloud for the research community.