Lyapunov Spectrum for Double Pendulum using GRU

A simple deep learning repository covering ANN, CNN, Transfer Learning, and Sequence Models (RNN, LSTM, GRU) with easy explanations of core concepts.

This project involved analyzing Amazon user reviews to determine the sentiment expressed (positive, negative, or neutral) using Recurrent Neural Networks (RNNs). The project leveraged deep learning techniques for text classification, processing and transforming the raw text data into a format suitable for RNN input.

This project implements a sequence-to-sequence (Seq2Seq) model using an RNN-based approach for machine translation from Urdu to English. The model utilizes PyTorch and processes a dataset of parallel Urdu-English sentences.

Adaptive Length Image Tokenization via Recurrent Allocation | How many tokens is an image worth ?

🤵 En nettside som bruker maskinlæring til å gjette om du er mann eller kvinne basert på hva du skriver 💃

In this model ,I have used the GLUE dataset in order to perform a sentiment analysis on the reviews as positive and negative. Different types of Neural Networks using TensorFlow was tried out such as CNN,GRU and bi-directional LSTMs. The accuracy of predictions of each of these models are visualised in the end

This repository provides a step-by-step implementation of a Gated Recurrent Unit (GRU) model from scratch using PyTorch.

LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow

A basic PyTorch RNN implementation for predicting the next value in a sine wave sequence, demonstrating RNNs' ability to model sequential data.

This project classifies stocks based on tick-by-tick order-book data, as part of the CFM challenge. The model uses GRU layers to process sequential market data and leverages feature engineering on prices, bid/ask sizes, and order types. It’s trained with cross-entropy loss using the Adam optimizer.

Stock Market Prediction on High-Frequency Data Using soft computing based AI models

estimate remaining useful life of CPSs, namely NASA's C-MAPSS, using a hybrid CNN-LSTM model.

Thesis for NTUA: Multivariate Computational Load Timeseries Forecasting & Resource Provisioning with Machine Learning Techniques

Piano music generation using recurrent neural network (RNN) model

The project aims to utilize deep learning models to forecast wind farm power output using CNN, LSTM, RNN, and GRU artificial neural networks.

Train Time Delay Prediction using machine learning

Public repository associated with: "MemoryInception: Predicting Neurological Recovery from EEG Using Recurrent Inceptions"