CaffeCat's Stars
nodesource/distributions
NodeSource Node.js Binary Distributions
GetStream/Winds
A Beautiful Open Source RSS & Podcast App Powered by Getstream.io
IvanMathy/Boop
A scriptable scratchpad for developers. In slow yet steady progress.
oldj/SwitchHosts
Switch hosts quickly!
giuspen/cherrytree
cherrytree
timqian/chinese-independent-blogs
中文独立博客列表
Eugeny/tabby
A terminal for a more modern age
didi/DoKit
一款面向泛前端产品研发全生命周期的效率平台。
loyinglin/LearnOpenGLES
OpenGL ES的各种尝试,有详细的博客。
shidenggui/easytrader
提供同花顺客户端/国金/华泰客户端/雪球的基金、股票自动程序化交易以及自动打新,支持跟踪 joinquant /ricequant 模拟交易 和 实盘雪球组合, 量化交易组件
akfamily/akshare
AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库
kungfu-origin/kungfu
Kungfu Trader
a7510774/macOSObjc-App-Dev-Ebook-Demo
MAC开发教程(Chapter 34不错,包含自动生成Icon 以及 自动设置多语言工具)
sbfkcel/towxml
微信小程序HTML、Markdown渲染库
icofans/iOS-Interview-Questions
iOS面试题整理,在线查看地址:https://ios.nobady.cn
CaffeCat/iOSDevNotesAndInterviews
🚴 iOS interview questions,git, dev notes, and more
hzlzh/Best-App
收集&推荐优秀的 Apps/硬件/技巧/周边等
selierlin/Share-SSR-V2ray
机场推荐/SSR V2ray节点订阅机场/镜像直连/工具推荐
devdawei/libstdc-
Xcode 10 之后删除的 libstdc++ 库
PacktPublishing/Learn-OpenCV-4-By-Building-Projects-Second-Edition
Learn OpenCV 4 By Building Projects, Second Edition, published by Packt
BoostIO/BoostNote-Legacy
This repository is outdated and new Boost Note app is available! We've launched a new Boost Note app which supports real-time collaborative writing. https://github.com/BoostIO/BoostNote-App
shu223/iOS-10-Sampler
Code examples for new APIs of iOS 10.
shadowsocks/shadowsocks-iOS
Removed according to regulations.
Jounce/Surge
A Swift library that uses the Accelerate framework to provide high-performance functions for matrix math, digital signal processing, and image manipulation.
Alamofire/Alamofire
Elegant HTTP Networking in Swift
realm/realm-swift
Realm is a mobile database: a replacement for Core Data & SQLite
loyinglin/LearnMetal
Metal 入门教程
borisbanushev/stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
justjavac/Programming-Alpha-To-Omega
从零开始学编程 系列汇总(从α到Ω)
justjavac/Google-IPs
:us: Google 全球 IP 地址库