Facial Recognition using supervised machine learning
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Updated
May 15, 2018 - C
Facial Recognition using supervised machine learning
The KAUST SVD (KSVD) is a high performance software framework for computing a dense SVD on distributed-memory manycore systems.
A parallelized implementation of Principal Component Analysis (PCA) using Singular Value Decomposition (SVD) in OpenMP for C. The procedure used is Modified Gram Schmidt algorithm. The method for Classical Gram Schmidt is also available for use.
Distributed-memory, double-precision, polar decomposition (QDWH/ZOLO-PD) of a dense matrix, svd (QDWH/ZOLOPD-SVD) of a dense matrix
Optimized Singular Value Decomposition (SVD) on an ARM machine
Cortex-M3 is run under QEMU for CLion testing
Machine Learning from scratch in C
Educational project: minimal SVD image compressor in C
Application of Singular Value Decomposition (SVD) in C++ for high-performance image compression and noise reduction.
This project implements the Singular Value Decomposition (SVD) algorithm from scratch in C programming language. Singular Value Decomposition is a cornerstone technique in linear algebra, used extensively in data science, signal processing, statistics, and machine learning.
Streamlining Software Design
The vectorized (AVX-512) batched singular value decomposition algorithm for matrices of order two.
Vectorization of the Jacobi-type methods for the SVD and the EVD
FFT-accelerated Singular Spectrum Analysis in C. SSA at the speed of FFT. Decompose time series into trend, seasonality & noise. O(N log N) Hankel matvec.
MIMO OFDM Block Diagram Modeling and Simulation for both Open Loop and Closed Loop MIMO OFDM systems.The modular architecture uses individual blocks (written in C) for the implementation of the various stages in modulation and demodulation. Subroutines ( C code) are also supplied. Paper on Theory and Architecture for Closed Loop MIMO with SVD.
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