mjclass
AI Researcher & Data Scientist. Blockchain. Financial Engineering. Curious by nature.
Paris, France
Pinned Repositories
deep-learning-time-series
List of papers, code and experiments using deep learning for time series forecasting
gansgen
This project consists of practicing differents research papers and approaches about Generative Adversarial Networks (GANs) to make new and innovative architectures.
Letter-Recognition-with-Deep-Learning
Deep neural network model with Keras
python-training
Python training for business analysts and traders
pytorch-sentiment-analysis
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
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.
mjclass's Repositories
mjclass/deep-learning-time-series
List of papers, code and experiments using deep learning for time series forecasting
mjclass/gansgen
This project consists of practicing differents research papers and approaches about Generative Adversarial Networks (GANs) to make new and innovative architectures.
mjclass/Letter-Recognition-with-Deep-Learning
Deep neural network model with Keras
mjclass/python-training
Python training for business analysts and traders
mjclass/pytorch-sentiment-analysis
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
mjclass/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.