Search Results for "python project"

Independent MIT-licensed server SDKs for the Fragment Donor public API in Python, TypeScript, PHP, .NET, Go, Rust and Ruby. The private production backend and its history are not part of this project. The SDKs cover username and Premium lookup, wallet balance, and intended Stars/Premium gifting through a form-urlencoded contract. Purchase calls are not retried automatically, and no idempotency guarantee is claimed.

one C/C++ file & header ==> 8 functions: - beep - tray notify popup - message & question - input & password - save file - open file(s) - select folder - color picker complements OpenGL Vulkan GLFW GLUT GLUI VTK SFML TGUI SDL Ogre Unity3d ION OpenCV CeGui MathGL GLM CPW GLOW Open3D ImGui MyGui GLT NGL STB Nuklear Fenster MicroUi & head-less programs NO INIT NO MAIN LOOP NO LINKING NO INCLUDE win (XP to 11) ASCII MBCS UTF-8 UTF-16 (wchar_t) - native dialogs osx/unix...

EVEAI is a Deep Learning Library based on python Keras and Tensorflow. EVEAI dll allows embedding inference images from keras models into user-written applications. Under Windows, the EVEAI training Tool provides services to train user specific image datasets and EVEAI dll provides services to existing Windows applications which support inference images. Please visit https://github.com/Hommoner/EVEAI for more information.

An ACME client library and PowerShell client for the .NET platform. This project implements a client library and PowerShell client for the ACME protocol. ACMESharp is interoperable with the CA server used by the Let's Encrypt project which is the reference implementation for the server-side ACME protocol. ACMESharp includes features comparable to the official Let's Encrypt client which is the reference implementation for the client-side ACME protocol. The ACMESharp client implementation is...

This project includes the implementation of a neural network MLP, RBF, SOM and Hopfield networks in several popular programming languages. The project also includes examples of the use of neural networks as function approximation and time series prediction. Includes a special program makes it easy to test neural network based on training data and the optimization of the network.