RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.
-
Updated
Mar 24, 2023 - Python
8000
RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.
This project provides implementations with Keras/Tensorflow of some deep learning algorithms for Multivariate Time Series Forecasting: Transformers, Recurrent neural networks (LSTM and GRU), Convolutional neural networks, Multi-layer perceptron
Monorepo for trading algorithms
Scripts for M-LARGE training and analysis
Pytorch implementation of a sentence sentiment classification model with CNN, RNN, RNF (Recurrent Neural Filter) and BERT
Algotrading toolkit using customizable strategies, genetic algorithms, and RNN-based strategies
RNN using LSTM layers in coded in Python using Keras to predict Google open stock prices.
DECtalk song generator using a simple RNN architecture
A comprehensive Deep Learning repository featuring Neural Networks, CNNs, RNNs, LSTMs, Transformers, Generative AI, and practical machine learning implementations in Python.
Predictive Modelling of Time Series Data using LSTM RNNs
A web app that visualizes California county vaccination patterns and predicts their future trends with LSTM networks.
AI project on Healthcare emergencies demand immediate and precise intervention. This is quick, accurate and guidance critical in saving lives. This project focuses on AI-powered system specifically designed for healthcare emergencies, enabling efficient, real-time responses through both voice and video calls.
This project focuses on the classification of neuromuscular diseases using EMG (Electromyography) signals through deep learning. EMG signals carry valuable muscle activity data, which are analyzed using a 1D CNN model to detect health conditions such as Myopathy, Neuropathy, and Healthy states.
This repository is a Pytorch implemented version for CRNN OCR model
Web app that visualizes California county vaccination patterns and predicts their future trends with LSTM networks.
This project uses sentiment analysis using tweepy and textblob and Deep Learning model, Long-Short Term Memory (LSTM) Recurrent neural network (RNN) algorithm to predict closing prices of stocks.
Add a description, image, and links to the rnn-lstm topic page so that developers can more easily learn about it.
To associate your repository with the rnn-lstm topic, visit your repo's landing page and select "manage topics."