MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
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Updated
Jul 4, 2024 - C++
MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
A Swift library that uses the Accelerate framework to provide high-performance functions for matrix math, digital signal processing, and image manipulation.
Imaging is a simple image processing package for Go
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
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).
Building Convolutional Neural Networks From Scratch using NumPy
Theory of digital signal processing (DSP): signals, filtration (IIR, FIR, CIC, MAF), transforms (FFT, DFT, Hilbert, Z-transform) etc.
[Unofficial] PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)
Understanding Convolution for Semantic Segmentation
ShuffleNet in PyTorch. Based on https://arxiv.org/abs/1707.01083
Tensorflow implementation of Gated Conditional Pixel Convolutional Neural Network
Image processing and manipulation in JavaScript
Implementation of 1D, 2D, and 3D FFT convolutions in PyTorch. Much faster than direct convolutions for large kernel sizes.
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
Efficient Sparse-Winograd Convolutional Neural Networks (ICLR 2018)
Audio DSP effects build on Android system framework layer. This is a repository contains a pack of high quality DSP algorithms specialized for audio processing.
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