marysalvana Goto Github PK

spatialfda

R code and dataset for the paper on spatially functional data

spatio-temporal-cross-covariance-functions-under-the-lagrangian-framework

stock-price-predictor

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.

stockast

Predict stock market pricing over 180 minutes using Black-Scholes stocastic modelling and parallel Monte-Carlo simulations.

stocknet

This repository contains the default partial but functional python's implementation of StockNet.

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.

stocks-price-prediction-using-multivariate-analysis

studious-potato

thesis files

superpiano

Google Colab for training music AI model and generating music with Transformer technology (Google XLNet)

tradingview-scraper

typhoon-impact-based-forecasting-model

Typhoon Impact forecasting model This tool was developed as trigger mechanism for the typhoon Early action protocol of the philipiness resdcross FbF project. The model will predict the potential damage of a typhoon before landfall, the prediction will be percentage of completely damaged houses per manuciplaity.

wavegan-pytorch

PyTorch implementation of " Synthesizing Audio with Generative Adversarial Networks"

zmcintegral

An easy way to use multi-GPUs to calculate multi-dimensional integration