Pinned Repositories
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.
menglin-cao's Repositories
menglin-cao/DA-RNN
menglin-cao/datasets
Stuff related to the BMClab public datasets
menglin-cao/RGAN
Recurrent (conditional) generative adversarial networks for generating real-valued time series data.
menglin-cao/sEMG_DeepLearning
sEMG-based gesture recognition using deep learnig
menglin-cao/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.
menglin-cao/t-SNE-master
使用sklearn的简单的tSNE,PCA,isomap,LLE方法可视化mnist
menglin-cao/tensorflow-windows-wheel
Tensorflow prebuilt binary for Windows
menglin-cao/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.
menglin-cao/Time-Series-Prediction
Exploring HMM, LSTM and Regression techniques to predict respiratory rate of an individual from accelerometer data.