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MicroTunsten's Projects

algo_ds_notes icon algo_ds_notes

It is a repository that is a collection of algorithms and data structures with implementation in various languages.

build-your-own-x icon build-your-own-x

Master programming by recreating your favorite technologies from scratch.

codinginterviewsnotes icon codinginterviewsnotes

涵盖C++ Primer 5th、 effective C++ 、 STL api和demos C++ 基础知识与理论、 智能指针、C++11、 Git教程 Linux命令 Unix操作系统(进程、线程、内存管理、信号)计算机网络、 数据结构(排序、查找)、数据库、、C++对象模型、 设计模式、算法(《剑指offer》、leetcode、lintcode、hihocoder、《王道程序员求职宝典》)、面试题、嵌入式相关等

cuda-learn-notes icon cuda-learn-notes

🎉CUDA 笔记 / 大模型手撕CUDA / C++笔记,更新随缘: flash_attn、sgemm、sgemv、warp reduce、block reduce、dot product、elementwise、softmax、layernorm、rmsnorm、hist etc.

cvpr21chal-slr icon cvpr21chal-slr

This repo contains the official code of our work SAM-SLR which won the CVPR 2021 Challenge on Large Scale Signer Independent Isolated Sign Language Recognition.

deeplabcut icon deeplabcut

Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans

filemanager icon filemanager

文件管理/文件去重/文件备份/文件同步/查找相似图片/查找相似视频/本地以图搜图/比对文本文件内容/拷贝目录结构/视频合并/视频裁剪/找出损坏的视频/提取音频/音频格式转换/批量重命名/搜索文件

openmlsys-cuda icon openmlsys-cuda

Tutorials for writing high-performance GPU operators in AI frameworks.

python icon python

All Algorithms implemented in Python

pytorchintroduction icon pytorchintroduction

《深入浅出 PyTorch——从模型到源码》源代码和勘误(见Issues)

slt icon slt

Sign Language Transformers (CVPR'20)

swin-transformer icon swin-transformer

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

torch-template-for-deep-learning icon torch-template-for-deep-learning

Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

torchtext-summary icon torchtext-summary

torchtext使用总结,从零开始逐步实现了torchtext文本预处理过程,包括截断补长,词表构建,使用预训练词向量,构建可用于PyTorch的可迭代数据等步骤。并结合Pytorch实现LSTM.

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