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A easy to use implementation of xLSTM

Energy consumption prediction using LSTM/GRU networks in PyTorch

This paper summarizes a deep learning-based approach with an LSTM trained on the widely used Oxford battery degradation dataset and the help of generative adversarial networks (GANS).

Sequence Models by Andrew Ng on Coursera. Programming Assignments and Quiz Solutions.

This code is to implement the IndRNN.

A project that trains a LSTM recurrent neural network over a data-set of MIDI files.

Predict bitcoin values using social sentiments (e.g. news and reddit posts)

Developed an AI-powered supermarket analytics system using LSTM, K-Means, and Apriori to predict sales, segment customers, recommend products, and optimize inventory through an agent-based architecture with a Streamlit dashboard.

TensorFlow reimplementation of Topic-to-Essay Generation with Neural Networks.

Predicting Weather using CNN-LSTM