Pinned Repositories
DeepComplexUNetPyTorch
Implementation of Deep Complex UNet Using PyTorch
GANs-for-Speech-Enhancement
Generative Adversarial Network implemented for the Time-Frequency based Speech Enhancement
n-beats
Pytorch/Keras implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
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.
aliaghababaei's Repositories
aliaghababaei/DeepComplexUNetPyTorch
Implementation of Deep Complex UNet Using PyTorch
aliaghababaei/GANs-for-Speech-Enhancement
Generative Adversarial Network implemented for the Time-Frequency based Speech Enhancement
aliaghababaei/n-beats
Pytorch/Keras implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
aliaghababaei/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.