murufeng's repositories
awesome_lightweight_networks
The implementation of various lightweight networks by using PyTorch. such as：MobileNetV2，MobileNeXt，GhostNet，ParNet，MobileViT、AdderNet，ShuffleNetV1-V2，LCNet，ConvNeXt，etc. ⭐⭐⭐⭐⭐
CVPR_2021_Papers
CVPR2021最新论文汇总，主要包括：Transformer, NAS，模型压缩，模型评估，图像分类，检测，分割，跟踪，GAN，超分辨率，图像恢复，去雨，去雾，去模糊，去噪，重建等等
Awesome-AI-algorithm
人工智能算法方面的综合资料合集:包括求职面试、机器学习、深度学习、强化学习等方面的资料和代码
awesome-papers
机器学习，深度学习，自然语言处理，计算机视觉方面的顶级期刊会议论文集
awesome-machine-learning
A curated list of awesome machine Learning tutorials,courses and communities.
Awesome_vision_transformer
Implementation of vision transformer. ⭐⭐⭐
deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
Awesome-NLP-Resources
自然语言处理方面资料集
Image-Classification
Implement a few key architectures for image classification by using neural network
interview_internal_reference
2019年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总。
knowledge_distillation
一款即插即用的知识蒸馏工具包
LeetCode-Algorithm
剑指Offer & LeetCode Problems' Solutions: A Record of My Problem Solving Journey（Python,C++)）
DeepLearning
深度学习入门教程&&优秀文章&&Deep Learning Tutorial
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
awesome-architecture-search
A curated list of awesome architecture search resources
awesome-automl-papers
A curated list of automated machine learning papers, articles, tutorials, slides and projects
coding-interview-university
A complete computer science study plan to become a software engineer.
cs231n.github.io
Public facing notes page
CtCI-6th-Edition
Cracking the Coding Interview 6th Ed. Solutions
deep_learning_theory_and_practice
《深度学习理论与实战：基础篇》代码
google-research
Google AI Research
lihang-code
《统计学习方法》的代码实现
murufeng.github.io
My Blog
pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
state-of-the-art-result-for-machine-learning-problems
This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.