MATLAB Code for Training Convolutional Neural Networks (CNN)

In machine learning, Convolutional Neural Networks (CNNs) are a widely utilized type of neural network architecture. CNNs excel at tasks including image classification, speech recognition, and natural language processing. The network structure employs specialized layers such as convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for final classification decisions. These layers work collectively to filter and extract hierarchical patterns from input data. In deep learning applications, CNNs play a pivotal role, with their performance and effectiveness demonstrated across numerous practical implementations.

MATLAB implementation typically involves using the Deep Learning Toolbox, where key functions include:
- convolution2dLayer() to define filter size and number of channels
- maxPooling2dLayer() for downsampling through maximum value selection
- fullyConnectedLayer() combined with softmaxLayer() for classification
Training algorithms often utilize stochastic gradient descent with momentum, implemented via trainingOptions() to configure learning rate schedules and batch size parameters.