RNN-LSTM Implementation in MATLAB

This project implements Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks using MATLAB programming language. RNN-LSTM represents a powerful neural network architecture particularly suitable for processing sequential data, including applications such as language modeling, time series prediction, and text generation tasks.

By leveraging MATLAB's Deep Learning Toolbox, we can efficiently construct RNN-LSTM models and utilize substantial computational capabilities for training and inference. During implementation, we will explore how to configure network architectures using functions like sequenceInputLayer, lstmLayer, and fullyConnectedLayer. The implementation covers selecting appropriate activation functions such as tanh or sigmoid for gates and ReLU for hidden layers, while employing loss functions like cross-entropy for classification or mean-squared-error for regression tasks.

The development process includes hyperparameter optimization techniques for adjusting learning rates, sequence lengths, and hidden unit counts using trainingOptions configuration. We will demonstrate batch processing of sequential data through mini-batch preparation and sequence padding methodologies. The implementation also addresses gradient clipping and dropout regularization to prevent overfitting in long sequence processing.

Upon completing this project, developers will gain deeper understanding of RNN-LSTM model mechanics and enhance proficiency with MATLAB's Deep Learning Toolbox, establishing a solid foundation for future deep learning projects involving sequential data analysis.