niufuquan1 Goto Github PK

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）

ai-job-recommend

国内公司人工智能方向（含机器学习、深度学习、计算机视觉和自然语言处理）岗位的招聘信息（含全职、实习和校招）

awesome-semantic-segmentation-pytorch

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

baike_spider

blog

to study django

bluelog

A blog engine built with Flask.

cnblogs-theme-simplememory

🍭 Cnblogs theme / Basic theme : SimpleMemory

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、Java、Python、C++

cvpr2019

cvpr2019 papers

daily-paper-computer-vision

记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文

dataforemail

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deep-learning-papers-translation

深度学习论文翻译，包括分类论文，检测论文等

free-programming-books-zh_cn

:books: 免费的计算机编程类中文书籍，欢迎投稿

fucking-algorithm

手把手撕LeetCode题目，扒各种算法套路的裤子。English version supported! Crack LeetCode, not only how, but also why.

hello-algorithm

🙈🙉🙊 本项目包括：1、我写的三十万字图解算法题典 2、100 张 IT 相关超清思维导图 3、100 篇大厂面经汇总 4、各语言编程电子书 100 本 5、English version supported !!! 🚀🚀 国人项目上榜首不易，右上角助力一波！干就对了，奥利给 ！🚀🚀

hellogithub

:octocat: Find pearls on open-source seashore 分享 GitHub 上有趣、入门级的开源项目

just-the-docs

A modern, high customizable, responsive Jekyll theme for documention with built-in search.

leetcode

:pencil2: LeetCode solutions in C++ 11 and Python3

leetcode-others

leetCode刷题之路的记录以及刷其他内容的记录

my_projects

Maybe a lab‘s Project Or Maybe myself

mystudy_for_sklearn_tensorflow

机器学习实战（基于scikit-learn和TensorFlow）》的学习心得

niufuquan1.github.io

pyda-2e-zh

:book: [译] 利用 Python 进行数据分析 · 第 2 版

python-100-days

Python - 100天从新手到大师

tensorflow-2.x-tutorials

TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。

tongeimagedataset

This tongue image data set contains 300 tongue images. All the images in the data set were acquired by our image acquisition device and the image size was 576*768. Manual segmentations were used as the ground truth.