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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.

❤️ Fredy - [F]ind [R]eal [E]state [D]amn Eas[y] - Fredy keeps searching for new apartments, houses, and flats in Europe on platforms like ImmoScout24, Immowelt, eBay Kleinanzeigen and instantly delivers the results to you via Slack, Telegram, Email, Discord or ntfy, so you can focus on the more important things in life ;)

:watch: Watch a directory for file changes (.cfc, .cfm, .xml) to trigger mxunit test suite full run, displaying results in the console

Brackets extension for standard, a simple linter with sensible defaults. No longer maintained, Try using https://github.com/zaggino/brackets-eslint and https://github.com/feross/eslint-config-standard