menglin-cao

DA-RNN

datasets

Stuff related to the BMClab public datasets

RGAN

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.

sEMG_DeepLearning

sEMG-based gesture recognition using deep learnig

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.

t-SNE-master

使用sklearn的简单的tSNE,PCA,isomap,LLE方法可视化mnist

tensorflow-windows-wheel

Tensorflow prebuilt binary for Windows

Time-Series-Forecasting-of-Amazon-Stock-Prices-using-Neural-Networks-LSTM-and-GAN-

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

Time-Series-Prediction

Exploring HMM, LSTM and Regression techniques to predict respiratory rate of an individual from accelerometer data.