MATLAB Implementation of Convolutional Neural Networks (CNN)

In the fields of machine learning and artificial intelligence, Convolutional Neural Networks (CNNs) represent a fundamental technology for tasks such as image recognition, speech recognition, and natural language processing. The operational principle involves a hierarchical architecture comprising convolutional layers, pooling layers, and fully-connected layers that collectively enable efficient data processing and feature extraction. The convolutional layer performs feature extraction through convolution operations using learned filters (kernels), which can be implemented in MATLAB using functions like conv2 or the convolution2dLayer in Deep Learning Toolbox. Pooling layers subsequently downsample the convolutional outputs to reduce computational complexity, typically implemented through operations like max-pooling (using maxpooling2dLayer) or average-pooling. Finally, fully-connected layers map the extracted features to output classifications, often implemented with fullyConnectedLayer followed by softmax activation for multi-class problems. CNNs find extensive applications across domains – in computer vision for object recognition, image classification (using architectures like AlexNet or ResNet), and object detection tasks (implemented with frameworks like YOLO or R-CNN), while in natural language processing they facilitate text classification, sentiment analysis, and machine translation through 1D convolutional implementations (convolution1dLayer). The MATLAB Deep Learning Toolbox provides comprehensive support for CNN implementation through layer graph construction (layerGraph), training with trainNetwork, and pretrained model deployment. Thus, CNNs have become indispensable in modern machine learning and AI applications.