Deep Learning Autoencoders with MATLAB Implementation

In this article, we will comprehensively discuss deep learning autoencoders and demonstrate their implementation using MATLAB programs. To achieve this objective, users must first download and configure essential files, including but not limited to: deep learning libraries, MATLAB compilers, and CUDA toolkits for GPU acceleration. The implementation involves defining encoder and decoder architectures using MATLAB's Deep Learning Toolbox functions like trainAutoencoder and stackedAutoencoder, which handle layer initialization and weight optimization through backpropagation algorithms. During programming, critical considerations include input data preprocessing (normalization/reshape), architectural design decisions (number of hidden layers, activation functions), and hyperparameter tuning (learning rates, regularization). The training process typically employs optimization methods like stochastic gradient descent with mini-batches to minimize reconstruction loss. Finally, we will illustrate the program's functionality through practical examples, showcasing feature extraction and dimensionality reduction capabilities to enhance understanding of deep learning autoencoder implementation methodologies.