Convolutional Neural Network (CNN) Algorithm in Deep Learning

Convolutional Neural Networks (CNN) represent one of the core algorithms in deep learning for processing image data. By simulating the human brain's visual perception mechanism, CNNs can automatically extract hierarchical features from images and are widely applied in areas such as image classification and object detection.

The core architecture of CNN consists of three key components: Convolutional Layer: Extracts local features through sliding window operations, using learnable filters to create feature maps from input images. This operation preserves spatial relationships between pixels while significantly reducing parameter count through weight sharing. Code implementation typically involves defining kernel size, stride, and padding parameters using frameworks like TensorFlow's Conv2D or PyTorch's nn.Conv2d. Pooling Layer: Commonly employs max pooling or average pooling to downsample feature maps, reducing data dimensionality while preserving important features and enhancing model robustness to positional variations. Implementation involves specifying pool size and stride parameters, with max pooling being preferred for preserving strong activation features. Fully Connected Layer: Combines extracted high-level features to produce final classification results, typically implemented using dense layers that connect all neurons from previous layers to output nodes.

Typical CNN implementation includes complete training and testing workflows. During training, backpropagation algorithm adjusts network parameters using loss functions like cross-entropy to measure prediction errors, combined with optimizers such as Adam for parameter updates. The training process involves forward propagation, loss calculation, and gradient descent optimization. Testing phase freezes network weights and performs only forward propagation to evaluate model performance on unseen data, implemented through model.eval() mode in deep learning frameworks.

Modern CNN architectures like ResNet introduce innovative structures such as skip connections, effectively solving the vanishing gradient problem in deep networks and enabling network depths of hundreds of layers. Practical applications require attention to techniques like data augmentation, learning rate scheduling, and regularization methods (Dropout, L2 regularization) to improve model generalization capability. Code implementation often involves using pre-trained models and transfer learning for specific computer vision tasks.