C Programming
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"These notes are part of the UW Experimental College course on Introductory C Programming. They are based on notes prepared (beginning in Spring, 1995) to supplement the book The C Programming Language, by Brian Kernighan and Dennis Ritchie, or K&R as the book and its authors are affectionately known. (The second edition was published in 1988 by Prentice-Hall, ISBN 0-13-110362-8.) These notes are now (as of Winter, 1995-6) intended to be stand-alone, although the sections are still cross-referenced to those of K&R, for the reader who wants to pursue a more in-depth exposition." C is a low-level programming language that provides a deep understanding of how a computer's memory and hardware work. This knowledge can be valuable when optimizing apps for performance or when dealing with resource-constrained environments.C is often used as the foundation for creating cross-platform libraries and frameworks. Learning C can allow you to develop libraries that can be used across different platforms, including iOS, Android, and desktop environments.
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.
Reinforcement Learning For Beginners with Python
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This course takes through the fundamentals required to get started with reinforcement learning with Python, OpenAI Gym and Stable Baselines. You'll be able to build deep learning powered agents to solve a varying number of RL problems including CartPole, Breakout and CarRacing as well as learning how to build your very own/custom environment!
AWS Tutorial For Beginners
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An AWS Tutorial for Beginners is a course that teaches the basics of Amazon Web Services (AWS), a cloud computing platform that offers a wide range of services, including compute, storage, networking, databases, analytics, machine learning, and artificial intelligence.
Wiki for Onboarding onto the C3DDB Cluster at MGHPCC
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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.
Harnessing the Power of Cloud and Machine Learning for Climate and Ocean Advances
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Documentation and presentation on how to use machine learning and deep learning framework using TensorFlow, Keras and sci-kit learn for Climate and Ocean Advances
Official Documentation for PyTorch and NumPy
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The official documentation for PyTorch, a machine learning tensor-based framework, and NumPy, which allows for support for ndarrays which is useful to make tensors when implementing NNs. Both libraries can be installed with pip.
The Official Documentation of Pandas
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Pandas is one of the most essential Python libraries for data analysis and manipulation. It provides high-performance, easy-to-use data structures, and data analysis tools for the Python programming language. The official documentation serves as an in-depth guide to using this powerful tool including explanations and examples.
Master’s in Cybersecurity Degree Essentials
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Offers comprehensive information on various master's degree options in cybersecurity, including program details, admission requirements, and career opportunities, helping students make informed decisions about pursuing an advanced degree in cybersecurity.
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.
How to use Rclone
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Learn how to use Rclone to transfer data, specifically from your local drive to the Open Storage Network, vice versa.
Raftlib: Open Source library for concurrent data processing pipelines
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Raftlib is an open-source C++ Library that provides a framework for implementing parallel and concurrent data processing pipelines. It is designed to simplify the development of high-performance data processing applications by abstracting away the complexities of parallelism, concurrency, and data flow management.
It enables stream/data-flow parallel computation by linking parallel compute kernels together using simple right shift operators, similar to C++ streams for string manipulation. RaftLib eliminates the need for explicit usage of traditional threading libraries such as pthreads, std::thread, or OpenMP, which can lead to non-deterministic behavior when misused.
Introduction to Parallel Computing Tutorial
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The tutorial is intended to provide a brief overview of the extensive and broad topic of Parallel Computing. It covers the basics of parallel computing, and is intended for someone who is just becoming acquainted with the subject .
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.
fast.ai
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Fastai offers many tools to people working with machine learning and artifical intelligence including tutorials on PyTorch in addition to their own library built on PyTorch, news articles, and other resources to dive into this realm.
Texas A&M HPRC Training Site
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Training Resources and Courses offered by Texas A&M's Research Computing Group
Neurostars
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A question and answer forum for neuroscience researchers, infrastructure providers and software developers.
Introductory Tutorial to Numpy and Pandas for Data Analysis
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In this tutorial, I present an overview with many examples of the use of Numpy and Pandas for data analysis. Beginners in the field of data analysis can find It incredibly helpful, and at the same time, anyone who already has experience in data analysis and needs a refresher can find value in it. I discuss the use of Numpy for analyzing 1D and 2D multidimensional data and an introduction on using Pandas to manipulate CSV files.
Numpy - a Python Library
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Numpy is a python package that leverages types and compiled C code to make many math operations in Python efficient. It is especially useful for matrix manipulation and operations.
Slurm User Group Mailing List
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MATLAB bioinformatics toolbox
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Bioinformatics Toolbox provides algorithms and apps for Next Generation Sequencing (NGS), microarray analysis, mass spectrometry, and gene ontology. Using toolbox functions, you can read genomic and proteomic data from standard file formats such as SAM, FASTA, CEL, and CDF, as well as from online databases such as the NCBI Gene Expression Omnibus and GenBank.
A visual introduction to Gaussian Belief Propagation
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This website is an interactive introduction to Gaussian Belief Propagation (GBP). A probabilistic inference algorithm that operates by passing messages between the nodes of arbitrarily structured factor graphs. A special case of loopy belief propagation, GBP updates rely only on local information and will converge independently of the message schedule. The key argument is that, given recent trends in computing hardware, GBP has the right computational properties to act as a scalable distributed probabilistic inference framework for future machine learning systems.
How to Build a Great Relationship with a Mentor
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Emphasizes benefits of being mentored. Describes how to identify and choose a mentor. Suggests a path forward. Not mentor or two-way focused.
Spack Documentation
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Spack is a package manager for supercomputers that can help administrators install scientific software and libraries for multiple complex software stacks.
Quick and Robust Data Augmentation with Albumentations Library
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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
