A Fortran-based feed-forward neural network library. Whilst this library currently has a focus on 3D convolutional neural networks (CNNs), it can handle most standard hidden layer forms of neural networks, with the plan to integrate more.

Official implementation of SPANet in ICCV2023

Library for specialized dense and sparse matrix operations, and deep learning primitives.

Plots absorption spectra from from ORCA output files

带你从零实现一个高性能的深度学习推理库，支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step

Pytorch implementation of various token mixers; Attention Mechanisms, MLP, and etc for understanding computer vision papers and other tasks.

This project is about convolution operator optimization on GPU, include GEMM based (Implicit GEMM) convolution.

Plots IR spectra from from ORCA output files

This project implements an algorithm for rotating images by an arbitrary angle using the matrix decomposition approach described in the paper "Convolution-based interpolation for fast, high-quality rotation of images" by M. Unser, P. Thevenaz, and L. Yaroslavsky (IEEE Transactions on Image Processing, 1995).

An implementation of the system-wide JamesDSP audio processing engine for non-rooted Android devices

American Sign Language (ASL) Detection using CNN

Upload of CUDA programs developed in my GPU Computing Course

This repository is related to all about Computer Vision - an A-Z guide to the world of Computer Vision. This supplement contains the implementation of algorithms, statistical methods, and techniques (in Python)

Time Series Classification Analysis of 21 algorithms on the UCR archive datasets + Introduction to a Convolution-based classifier with Feature Selection

Elementary cellular automata CLI

Machine Intelligence Shader Autogen. AMDGPU ML shader code generator. (previously iGEMMgen)

MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba

Benchmark convolution implementations in C++ with Catch2 visualized with Vega-Lite

Course project for Linear Algebra course, Motion Detection with Optical Flow and PCA

Personal solutions to some of the questions from chapter 2 of "Signals and Systems: A Primer with MATLAB" by Matthew N. O. Sadiku and Warsame Hassan Ali.