decoderkurt,Kurt Koo,github

slm-lab

Modular Deep Reinforcement Learning framework in PyTorch.

spinningup

An educational resource to help anyone learn deep reinforcement learning.

spinningup-workshop

For educational materials related to the spinning up workshops.

stable-baselines

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

stable-baselines3

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

startup-success-prediction

An interactive machine learning and data visualization project

stellar-wind

Unique music composed by a decentralized organism - the Stellar transaction network

stock-prediction-models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

stock-prediction-xgboost

Stock Prediction with XGBoost: A Technical Indicators' approach

stock-price-prediction

Predicting stock price using historical data of a company, using Neural networks (LSTM).

stock-rnn

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

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.