Mehdi Paak (MehdiPaak)

MehdiPaak

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Location:Canada

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Mehdi Paak's repositories

aimet

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

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ae_git

run auto-encoder on clearml

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APPIAN

APPIAN is an open-source automated software pipeline for analyzing PET images in conjunction with MRI. The goal of APPIAN is to make PET tracer kinetic data analysis easy for users with moderate computing skills and to facilitate reproducible research.

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deploying-machine-learning-models

Deployment of Machine Learning Models

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pytorch-grad-cam

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

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ANN-function-approximator

To test the deep artificial neural networks as a universal function approximator using TensorFlow.

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Time-Series-Forecast-Women-Employment-Data

A workflow to analyze and understand time series data, from data cleaning and stationarity to modelling and forecasting

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Advanced-Vector-Extensions-AVX-vectorization

This program contains AVX functions to calculate the Euclidean distance between vectors using intel's YMM register intrinsics.

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FE-shell-mesh-parser

a command line program to parse finite element surface mesh in Nastran or Dyna format. Mesh can contain Triangular and/or Quad shell/surface elements.

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Sparse-Matrix-Reordering

A comparison between several reordering algorithms for minimal non-zero fill-ins in the Cholesky factorization when solving Ax=b.

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GPU-sum-square

OpenMP parallelization of multiple GPUs; the sum of square kernel is used to test the performance.

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Outlier-Detection

some outlier detection methods are explained and their use case is illustrated.

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PyPlate

PyPlate is a program that uses Salome-Meca and Code Aster to simulate the bending of a Kirchohhff plate (i.e. no transverse shear) of rectangular shape under uniform pressure

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Sensitivity-Analysis

This is a Python (Jupyter) notebook in which I demonstrate how to perform a sensitivity analysis based on a linear regression model.

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