17YangFang12's Stars
Snailclimb/JavaGuide
「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试,首选 JavaGuide!
singgel/JAVA_LINE
JAVA进阶相关书籍:《JAVA并发编程实践》、《Linux Shell脚本攻略》、《spring揭秘 精选版》、《高性能Mysql》、《深入理解Java虚拟机[JVM高级特性与最佳实践](周志明)》、《图解HTTP 彩色版》、《图解TCP_IP_第5版》、《head+first+servlets jsp》、《How Tomcat Works 中文版》、《J2EE核心模式》、《JAVA并发编程实践》一些大的上传不上来的文件在README
labuladong/fucking-algorithm
刷算法全靠套路,认准 labuladong 就够了!English version supported! Crack LeetCode, not only how, but also why.
CyC2018/CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计
zhedahht/CodingInterviewChinese2
《剑指Offer:名企面试官精讲典型编程面试题》第二版源代码
amusi/AI-Job-Notes
AI算法岗求职攻略(涵盖准备攻略、刷题指南、内推和AI公司清单等资料)
huggingface/pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
weiaicunzai/awesome-image-classification
A curated list of deep learning image classification papers and codes
jackcywang/Xian-Huawei-Al-competion
Code for 2019 xian-huawei-al-competition,we got 42th place(top5%)
jackcywang/vgg16
a simple tutorial of using vgg16 to classify images
jackcywang/Radio-classification
Baseline of Signal Classification Competition
zdaiot/AI-Competition-HuaWei
My solution to the 华为云人工智能创新应用大赛, which got the 32th place. (Top4%)
zdaiot/Kaggle-Steel-Defect-Detection
My solution to the Severstal: Steel Defect Detection on Kaggle, which got the 96th place. (Top4%)
CarryHJR/remote-sense-quickstart
4uiiurz1/pytorch-res2net
PyTorch implementation of Res2Net
Omar-Talabay/Udacity_MLND
udacity machine learning exercises.
chaozhong2010/SENet-PyTorch
This is the PyTorch1.0 implement of SENet to train on NWPU-RESISC45 dataset
zdaiot/rssrai2019_scene_classification
Code for rssrai2019 scene classification
Magic-chao/rssrai2019_scene_classification
Scene classification baseline. Test Acc:90.14%
aleju/imgaug
Image augmentation for machine learning experiments.
dsgiitr/d2l-pytorch
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.