A Convolutional Neural Network, or CNN, is a specialized type of artificial neural network designed for processing grid-like data, particularly images. CNNs are highly effective for image recognition, object detection, and various computer vision tasks. The network consists of multiple layers that progressively extract and learn features from the input data.
The convolutional layer is the core building block of a CNN. It applies filters, also called kernels, to the input data to extract important features like edges, textures, and patterns. The filter slides across the input, performing element-wise multiplication and summation to produce a feature map. Multiple filters can be applied to create multiple feature maps, each detecting different types of features.
After convolution, the activation layer applies a non-linear function like ReLU, which sets all negative values to zero while keeping positive values unchanged. This introduces non-linearity, allowing the network to learn complex patterns. The pooling layer then reduces the spatial dimensions of the feature maps while retaining the most important information. Max pooling, for example, takes the maximum value from each region, reducing computation and controlling overfitting.
The fully connected layer flattens the output from the convolutional and pooling layers and connects every neuron to every neuron in the previous layer. This layer performs high-level reasoning by combining the features extracted by the earlier layers. Finally, the output layer uses an activation function like Softmax to produce a probability distribution over the possible classes, giving us the final classification result.
To summarize what we have learned: Convolutional Neural Networks are specialized neural networks designed for image processing tasks. They use convolutional layers to extract features with filters, activation layers to introduce non-linearity for complex pattern learning, pooling layers to reduce dimensions while preserving important information, and fully connected layers to perform the final classification. This architecture makes CNNs highly effective for computer vision applications.