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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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  • Show all (20)
  • (-) vectorization (3)
  • ai (2)
  • big-data (2)
  • data-analysis (2)
  • deep-learning (1)
  • documentation (1)
  • image-processing (1)
  • machine-learning (1)
  • monte-carlo (1)
  • neural-networks (1)
  • optimization (1)
  • parallelization (1)
  • performance-tuning (1)
  • plotting (1)
  • visualization (1)

Topics

  • Show all (20)
  • (-) vectorization (3)
  • ai (2)
  • big-data (2)
  • data-analysis (2)
  • deep-learning (1)
  • documentation (1)
  • image-processing (1)
  • machine-learning (1)
  • monte-carlo (1)
  • neural-networks (1)
  • optimization (1)
  • parallelization (1)
  • performance-tuning (1)
  • plotting (1)
  • visualization (1)

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Scikit-Learn: Easy Machine Learning and Modeling
0
  • 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.
documentationaiplottingvisualizationbig-datadata-analysisdeep-learningimage-processingmachine-learningmonte-carloneural-networksvectorization
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Type
tool
Level
Beginner, Intermediate
Numba: Compiler for Python
0
  • Numba Compiler
Numba is a Python compiler designed for accelerating numerical and array operations, enabling users to enhance their application's performance by writing high-performance functions in Python itself. It utilizes LLVM to transform pure Python code into optimized machine code, achieving speeds comparable to languages like C, C++, and Fortran. Noteworthy features include dynamic code generation during import or runtime, support for both CPU and GPU hardware, and seamless integration with the Python scientific software ecosystem, particularly Numpy.
vectorizationoptimizationperformance-tuningparallelization
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Type
documentation
Level
Intermediate, Advanced
Introductory Tutorial to Numpy and Pandas for Data Analysis
0
  • Numpy and Pandas for Data Analysis
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.
aibig-datadata-analysisvectorization
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Type
documentation
Level
Beginner