• Conda Tutorial

Conda is a popular package management system. This tutorial introduces you to Conda and walks you through managing Python, your environment, and packages.

• 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.

• 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.

• NERSC Training and Tutorials Main Site
• NERSC Upcoming and Recent Training Events
• NERSC Archived Training and Tutorials

A comprehensive collection of NERSC developed training and tutorial events, offered on regular schedules. All sessions are archived, including slide decks, video recordings, and software examples as are available. Some examples of past training and tutorial topics are listed below Deep Learning for Sciences Webinar Series BerkeleyGW Tutorial Workshop VASP Trainings Timemory Software Monitoring Tutorial, April 2021 HPCToolkit to Measure and Analyzing GPU Applications Performance Tutorial Totalview Tutorial NVidia HPCSDK - OpenMP Target Offload Training Parallelware Training Series ARM Debugging and Profiling Tools Tutorial Roofline on NVIDIA GPUs GPUs for Science events 3-part OpenACC Training Series 9-part CUDA Training Series

• Representation Learning in Deep Learning

Representation learning is a fundamental concept in machine learning and artificial intelligence, particularly in the field of deep learning. At its core, representation learning involves the process of transforming raw data into a form that is more suitable for a specific task or learning objective. This transformation aims to extract meaningful and informative features or representations from the data, which can then be used for various tasks like classification, clustering, regression, and more.

• Overview
• Cookbook

The Biopython Tutorial and Cookbook website is a dedicated online resource for users in the field of computational biology and bioinformatics. It provides a collection of tutorials and practical examples focused on using the Biopython library. The website offers a series of tutorials that cover various aspects of Biopython, catering to users with different levels of expertise. It also includes code snippets and examples, and common solutions to common challenges in computational biology.

• Why Mentoring Matters and How to Get Started

Describes effective mentorship (both ways).

• "A Successful Git Branching Model"
• "Git Flight Rules"

A couple of resources that: 1.) Presents and defends a git branching workflow for stable collaborative git based projects. ("A Successful Git Branching Model") 2.) Maps "What do you want to do?" to the commands necessary to accomplish it. ("Git Flight Rules")

• Neocortex Documentation

Neocortex is a new supercomputing cluster at the Pittsburgh Supercomputing Center (PSC) that features groundbreaking AI hardware from Cerebras Systems.

• Extended Reality (XR) Resource workshop/Guide for Building collaboration

Comprehensive Extended Reality (XR) collaboration resources for building a high performance extended reality (XR), augmented reality (AR), virtual reality (VR) and mixed reality campus teams. The tags set are a small subset of the the topics covered.

• HPCwire

HPCwire is a prominent news and information source for the HPC community. Their website offers articles, analysis, and reports on HPC technologies, applications, and industry trends.

• High Performance Computing (HPC) 101 Cluster

High Performance Computing (HPC) Cluster

• 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.

• 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

• 3Blue1Brown Neural Networks

This video by the YouTube channel 3Blue1Brown provides a very simplified introduction to the theory behind neural networks. This tutorial is perfect for those that don't have much linear algebra or machine learning background and are eager to step into the realm of ML!

• 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).

• Rockfish Resources and Documentation

Resources and User Guide available at Rockfish

• Creating a Mobile Application

Goes through in detail on how to build an application that can run on Android and IOS devices, using Qt Creator to develop Qt Quick applications. Goes through the setting up, creation, configuration, optimization, and overall deployment. This provides the fundamental basis, need to click around on the site for more specifics.

• Research Security Operations Center

The NSF-funded ResearchSOC helps make scientific computing resilient to cyberattacks and capable of supporting trustworthy, productive research through operational cybersecurity services, training, and information sharing necessary to a community as unique and variable as research and education (R&E). ResearchSOC is a service offering from Indiana University's OmniSOC.

• Trinity Tutorial for Transcriptome Assembly

Trinity is one of the most popular tool to assemble transcripts from RNA-Seq short reads. In this tutorial, we will cover the basic usage of Trinity, best practice and common problems.

• Fairness and Machine Learning

The "Fairness and Machine Learning" book offers a rigorous exploration of fairness in ML and is suitable for researchers, practitioners, and anyone interested in understanding the complexities and implications of fairness in machine learning.

• DeepChem Tutorial

DeepChem is an open-source library built on TensorFlow and PyTorch. It is helpful in applying machine learning algorithms to molecular data.

• What are Geodetic Datums?

Often when working with GIS, or spatial data, one encounters the word "datum" and it may require that you choose a "datum" when doing GIS computation tasks. Below is a short video on what are datums from NOAA and UCAR.

• WarpX website

WarpX is an advanced particle-in-cell code used to model particle accelerators, which needs to be run on HPC. This website contains the tutorial on how to build WarpX on various HPC systems such as NERSC along with examples on how to set up post-processing/visualization tools for different physics cases.

• Docs

MediaPipe is Google's open-source framework for building multimodal (e.g., video, audio, etc.) machine learning pipelines. It is highly efficient and versatile, making it perfect for tasks like gesture recognition. This is a tutorial on how to make a custom model for gesture recognition tasks based on the Google MediaPipe API. This tutorial is specifically for video-playback, though could be generalized to image and live-video feed recognition.