convnets

Convolutional Neural Networks, often shortened to ConvNets or CNNs, are a class of artificial neural networks that have proven highly effective in processing data with a grid-like topology, such as images. They are inspired by the biological visual cortex, where individual neurons respond to stimuli only in a restricted region of the visual field known as the receptive field.

The core of a ConvNet is the convolutional layer. This layer applies a set of learnable filters (also known as kernels) to the input data. Each filter slides across the input, performing a dot product at each position. This operation produces a feature map, which highlights specific features in the input, such as edges, corners, or textures. Multiple filters can be used in a single convolutional layer, allowing the network to learn a diverse set of features.

Following the convolutional layers, pooling layers are typically employed. These layers reduce the spatial dimensions (width and height) of the feature maps, which helps to decrease computational complexity and control overfitting. Common pooling operations include max pooling and average pooling.

Activation functions, such as the Rectified Linear Unit (ReLU), are applied after convolutional layers to introduce non-linearity into the model, enabling it to learn more complex patterns.

Finally, fully connected layers are usually placed at the end of the network. These layers take the high-level features extracted by the convolutional and pooling layers and use them to perform classification or other tasks. The output of the fully connected layers is typically passed through a softmax function for multi-class classification problems. ConvNets are fundamental to modern computer vision tasks like image recognition, object detection, and segmentation.