A convolutional neural network (CNN) is a specialized type of neural network primarily designed for processing grid-like data, such as images. CNNs operate by applying convolutional operations to the input data to automatically detect patterns, edges, and textures at various levels of abstraction. A CNN consists of multiple layers, each performing distinct functions: convolutional layers, pooling layers, and fully connected layers. The convolutional layers extract features by sliding small filters (also known as kernels) over the input image, performing element-wise multiplication, and summing the results. This process enables the network to detect features like edges in the initial layers and more complex patterns, such as shapes or objects, in deeper layers.
Pooling layers follow convolutional layers, reducing the spatial dimensions of the feature maps while retaining important information. For instance, max pooling takes the maximum value from a specified region of the feature map, which effectively reduces the number of parameters and computation in the network while providing some level of translation invariance. By down-sampling the feature maps, pooling layers help the CNN to focus on the dominant features, making it more robust to variations in the input data, such as shifts and distortions.
Finally, the fully connected layers in a CNN act like traditional neural networks. After processing through convolutional and pooling layers, the feature maps are flattened into a single vector and then passed through one or more fully connected layers. These layers perform the final classification or regression tasks based on the features extracted from the input. For example, in an image classification task, the output layer might use a softmax activation function to assign probabilities to various classes, allowing the model to predict the most likely label for the input image. By combining these components, CNNs can effectively learn hierarchical representations of data, enabling them to excel in tasks involving visual information.