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Knowledge Base Resources

These resources are contributed by researchers, facilitators, engineers, and HPC admins. Please upvote resources you find useful!
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Topics

  • machine-learning (50)
  • ai (45)
  • training (41)
  • data-analysis (40)
  • deep-learning (28)
  • documentation (28)
  • big-data (26)
  • neural-networks (24)
  • workforce-development (21)
  • professional-development (18)
  • visualization (18)
  • parallelization (16)
  • community-outreach (14)
  • programming (14)
  • image-processing (13)
  • cybersecurity (12)
  • gpu (12)
  • r (12)
  • pytorch (11)
  • slurm (10)
  • c (9)
  • cloud-computing (9)
  • compiling (9)
  • mpi (9)
  • plotting (9)
  • administering-hpc (8)

Topics

  • machine-learning (50)
  • ai (45)
  • training (41)
  • data-analysis (40)
  • deep-learning (28)
  • documentation (28)
  • big-data (26)
  • neural-networks (24)
  • workforce-development (21)
  • professional-development (18)
  • visualization (18)
  • parallelization (16)
  • community-outreach (14)
  • programming (14)
  • image-processing (13)
  • cybersecurity (12)
  • gpu (12)
  • r (12)
  • pytorch (11)
  • slurm (10)
  • c (9)
  • cloud-computing (9)
  • compiling (9)
  • mpi (9)
  • plotting (9)
  • administering-hpc (8)

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Campus Champions Home Page
0
  • Campus Champions Home
Campus Champions foster a dynamic environment for a diverse community of research computing and data professionals sharing knowledge and experience in digital research infrastructure.
community-outreachprofessional-development
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Type
website
Level
Beginner, Intermediate, Advanced
PyTorch Introduction
0
  • PyTorch Tutorial at Stanford University
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.
deep-learningmachine-learningneural-networkspytorchpython
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Type
website
Level
Intermediate
Practical Machine Learning with Python
0
  • Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5
This video series provides a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. It covers topics such as linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. Goes over the high level intuitions of the algorithms and how they are logically meant to work. Apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn.
machine-learningprogrammingpython
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Type
video_link
Level
Advanced

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