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Robert Neumann's Projects

fastapi icon fastapi

FastAPI framework, high performance, easy to learn, fast to code, ready for production

getaviz icon getaviz

With Getaviz you can solve software engineering problems visually by exploring software artifacts them in 2D, 3D, and virtual reality.

headlamp icon headlamp

A Kubernetes web UI that is fully-featured, user-friendly and extensible

instabot icon instabot

Cool Instagram scripts, bots and API wrapper. Forever free. Written in Python.

kubernetes-gpu-guide icon kubernetes-gpu-guide

This guide should help fellow researchers and hobbyists to easily automate and accelerate there deep leaning training with their own Kubernetes GPU cluster.

kubespray icon kubespray

Deploy a Production Ready Kubernetes Cluster

lapsepitouch icon lapsepitouch

Touchscreen Timelapse controller for Raspberry Pi and Adafruit PiTFT

pcweb icon pcweb

A public repo of the Pynecone.io website.

serialapi icon serialapi

Java-API for a user-friendly, multi-threaded, synchronous and asynchronous communication with a RX/TX-interface.

stockpredictionai icon 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.

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