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avrocsvbundle

A Symfony2 bundle for importing and exporting entities with CSV files

bitburner-scipts

c2py-agh

C2Py is a tool to convert C code to Python code.

freepickem

Football pickem website I through together for some friends

godot_game

Testing godot game engine

keras2cpp

it's a small library for running trained Keras 2 models from a native C++ code.

kociemba

A pure Python and pure C ports of Kociemba's algorithm for solving Rubik's cube

lets-play-bitburner

lichess-bot

A bridge between Lichess API and chess engines

lstm-neural-network-for-time-series-prediction

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

machine-learning-for-trading

Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading

mongoose-pubsub

Pub/Sub built on Mongoose/MongoDB tailable cursors

nnfx-backtrader

No Nonsense Forex Backtrader Strategy with Custom Indicator Studies

predictions

premake-core

Premake

rubik

Efficient Rubik's cube solver in C++ with OpenGL visualizer. Implemented Thisletwaite's algorithm along with pattern database

rubikscube-twophasesolver

Solve Rubik's Cube in less than 20 moves on average with Python

rubix

Little program to solve a Rubik's cube.

semantic-release

:package::rocket: Fully automated version management and package publishing

simple-chess-ai

A simple chess AI

sotamatwsjtskimmer

A Plugin for WSJT-X and SparkSDR that filters reception reports and forwards potential SOTAmat messages to the SOTAmat server

sportacus

AYSO Soccer Referee Scheduling System

sportacus-dev

Sports (currently AYSO soccer) referee assignment website

sportacus2-blazor-client

sportacus2-blazor-client

sportacus2-server

NodeJS API server backend for Sportacus version 2

sportacus2-webclient

A basic web client mainly for debugging the server. Should also serve as an example of how to use the API.

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.