A Convolutional Neural Network (CNN) is a type of deep learning model designed primarily for processing grid-like data, such as images. Unlike standard neural networks, which treat input data as flat vectors, CNNs preserve the spatial structure of the input (e.g., the height, width, and channels of an image) and use specialized layers to detect patterns hierarchically. The core idea is to apply filters (or kernels) that slide over the input to extract features like edges, textures, or shapes. This approach reduces the number of parameters compared to fully connected networks, making CNNs computationally efficient for tasks involving high-dimensional data like images.
CNNs are built using three key layer types: convolutional, pooling, and fully connected. Convolutional layers apply filters to the input, generating feature maps that highlight where specific patterns appear. For example, in an image classification task, the first layers might detect edges, while deeper layers recognize complex structures like eyes or wheels. Pooling layers (e.g., max pooling) downsample these feature maps, reducing their spatial dimensions and making the model more robust to small shifts in the input. After several convolutional and pooling layers, the output is flattened and passed to fully connected layers for classification. A classic example is using CNNs on the MNIST dataset, where they achieve high accuracy in digit recognition by learning hierarchical features from pixel data.
Developers often implement CNNs using frameworks like TensorFlow or PyTorch. For instance, a simple CNN in PyTorch might include Conv2d layers with ReLU activation, followed by MaxPool2d layers, and ending with linear layers for prediction. Hyperparameters like filter size (e.g., 3x3), stride (how far the filter moves), and padding (to handle edges) are critical to tune. CNNs also benefit from techniques like data augmentation (e.g., rotating or flipping images) to improve generalization. Pretrained models like ResNet or VGG16 are widely used for transfer learning, where a model trained on a large dataset (e.g., ImageNet) is fine-tuned for a specific task with limited data. While CNNs excel in computer vision, they’re also applied to tasks like time-series analysis or natural language processing by adapting the input structure.