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Welcome to the home of the Hot Chocolate GraphQL server for .NET, the Strawberry Shake GraphQL client for .NET and Nitro the awesome Monaco based GraphQL IDE.

OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

  • point-cloud
  • 3d-detection
  • autonomous-driving
  • pv-rcnn

ClosedXML is a .NET library for reading, manipulating and writing Excel 2007+ (.xlsx, .xlsm) files. It aims to provide an intuitive and user-friendly interface to dealing with the underlying OpenXML API.

Advanced usage of UIAlertController and pickers based on it: Telegram, Contacts, Location, PhotoLibrary, Country, Phone Code, Currency, Date...

  • uialertcontroller
  • picker
  • image-picker
  • pickerview

A highly configurable Windows status bar written in Python.

  • python
  • ricing
  • status-bar
  • statusbar

Litmus helps SREs and developers practice chaos engineering in a Cloud-native way. Chaos experiments are published at the ChaosHub (https://hub.litmuschaos.io). Community notes is at https://hackmd.io/a4Zu_sH4TZGeih-xCimi3Q

mlpack: a fast, header-only C++ machine learning library

  • machine-learning-library
  • c-plus-plus
  • deep-learning
  • nearest-neighbor-search

SkiaSharp is a cross-platform 2D graphics API for .NET platforms based on Google's Skia Graphics Library. It provides a comprehensive 2D API that can be used across mobile, server and desktop models to render images.

A cross-platform file change monitor with multiple backends: Apple macOS File System Events, *BSD kqueue, Solaris/Illumos File Events Notification, Linux inotify and fanotify, Microsoft Windows and a stat()-based backend.

  • event-notifications
  • change-monitor
  • fswatch
  • kqueue

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.