Scikit-Learn: Easy Machine Learning and Modeling
0
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.
FSL Lectures
0
This is the official University of Oxford FSL group lecture page. This includes information on upcoming and past courses (online and in-person), as well as lecture materials. Available lecture materials includes slides and recordings on using FSL, MR physics, and applications of imaging data.
Quick and Robust Data Augmentation with Albumentations Library
0
Data augmentation is a crucial step in the pipeline for image classification with deep learning. Albumentations is an extremely versatile Python library that can be used to easily augment images. Transformations include rotations, flips, downscaling, distortions, blurs, and many more.
Citation:
Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA. Albumentations: Fast and Flexible Image Augmentations. Information. 2020; 11(2):125. https://doi.org/10.3390/info11020125
Performance Engineering Of Software Systems
0
A class from MITOpenCourseware that gives a hands on approach to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, caching optimizations, parallel programming, and building scalable systems.
An Introduction to the Julia Programming Language
0
The Julia Programming Language is one of the fastest growing software languages for AI/ML development. It writes in manner that's similar to Python while being nearly as fast as C++, while being open source, and reproducible across platforms and environments. The following link provide an introduction to using Julia including the basic syntax, data structures, key functions, and a few key packages.
Gaussian 16
0
Gaussian 16 is a computational chemistry package that is used in predicting molecular properties and understanding molecular behavior at a quantum mechanical level.
Hour of Ci
0
Hour of Cyberinfrastructure (Hour of CI) is a nationwide campaign to introduce undergraduate and graduate students to cyberinfrastructure and geographic information science (GIS).
Framework to help in scaling Machine Learning/Deep Learning/AI/NLP Models to Web Application level
0
This framework will help in scaling Machine Learning/Deep Learning/Artificial Intelligence/Natural Language Processing Models to Web Application level almost without any time.
Wiki for Onboarding onto the C3DDB Cluster at MGHPCC
0
This is a resource for researchers and students looking to on-board onto the c3ddb cluster at MGHPCC. In the code section, there are example job submission scripts for the different queues on c3ddb.
Scipy Lecture Notes
0
Comprehensive tutorials and lecture notes covering various aspects of scientific computing using Python and Scipy.
Language models and using HPC resources
0
Documentation and research based on the latest NLP text generation detection methods for 2023.
Texas A&M HPRC Training Site
0
Training Resources and Courses offered by Texas A&M's Research Computing Group
Astronomy data analysis with astropy
0
Astropy is a community-driven package that offers core functionalities needed for astrophysical computations and data analysis. From coordinate transformations to time and date handling, unit conversions, and cosmological calculations, Astropy ensures that astronomers can focus on their research without getting bogged down by the intricacies of programming. This guide walks you through practical usage of astropy from CCD data reduction to computing galactic orbits of stars.
Weka
0
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.
Molecular Dynamics Tutorials for Beginner's
0
Links to MD tutorials for beginner's across various simulation platforms.
Use Windows Subsystem for Linux for HPC Command Line Access from Windows
0
Windows Subsystem for Linux (WSL) provides a Linux environment for Windows users to access HPC resources fast and efficiently.
Cyber Security
0
learning cybersecurity is crucial for personal protection, safeguarding digital assets, financial security, and national security. It is important when it comes to consumer data protection for business, creating long lasting relationships with customers.
AI Institutes Cyberinfrastructure Documents: SAIL Meeting
0
Materials from the SAIL meeting (https://aiinstitutes.org/2023/06/21/sail-2023-summit-for-ai-leadership/). A space where AI researchers can learn about using ACCESS resources for AI applications and research.
Neural Networks in Julia
0
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.
Research Software Development in JupyterLab: A Platform for Collaboration Between Scientists and RSEs
0
Iterative Programming takes place when you can explore your code and play with your objects and functions without needing to save, recompile, or leave your development environment. This has traditionally been achieved with a REPL or an interactive shell. The magic of Jupyter Notebooks is that the interactive shell is saved as a persistant document, so you don't have to flip back and forth between your code files and the shell in order to program iteratively.
There are several editors and IDE's that are intended for notebook development, but JupyterLab is a natural choice because it is free and open source and most closely related to the Jupyter Notebooks/iPython projects. The chief motivation of this repository is to enable an IDE-like development environment through the use of extensions. There are also expositional notebooks to show off the usefulness of these features.
TensorFlow for Deep Neural Networks
0
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.
Automated Machine Learning Book
0
The authoritative book on automated machine learning, which allows practitioners without ML expertise to develop and deploy state-of-the-art machine learning approaches. Describes the background of techniques used in detail, along with tools that are available for free.
NCSA HPC Training Moodle
0
Self-paced tutorials on high-end computing topics such as parallel computing, multi-core performance, and performance tools. Other related topics include 'Cybersecurity for End Users' and 'Developing Webinar Training.' Some of the tutorials also offer digital badges. Many of these tutorials were previously offered on CI-Tutor. A list of open access training courses are provided below.
Parallel Computing on High-Performance Systems
Profiling Python Applications
Using an HPC Cluster for Scientific Applications
Debugging Serial and Parallel Codes
Introduction to MPI
Introduction to OpenMP
Introduction to Visualization
Introduction to Performance Tools
Multilevel Parallel Programming
Introduction to Multi-core Performance
Using the Lustre File System
Examples of Thrust code for GPU Parallelization
0
Some examples for writing Thrust code. To compile, download the CUDA compiler from NVIDIA. This code was tested with CUDA 9.2 but is likely compatible with other versions. Before compiling change extension from thrust_ex.txt to thrust_ex.cu. Any code on the device (GPU) that is run through a Thrust transform is automatically parallelized on the GPU. Host (CPU) code will not be. Thrust code can also be compiled to run on a CPU for practice.
Docker - Containerized, reproducible workflows
0
Docker allows for containerization of any task - basically a smaller, scalable version of a virtual machine. This is very useful when transferring work across computing environments, as it ensures reproducibility.