Konvoluutioneverkot

Konvoluutioneverkot, often abbreviated as CNNs, are a class of deep learning neural networks primarily used for visual recognition tasks. 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 idea behind CNNs is the use of convolutional layers. These layers apply a set of learnable filters (also called kernels) to the input data, typically an image. Each filter slides across the input, performing a dot product operation, which generates a feature map. These feature maps highlight specific patterns or features in the input, such as edges, corners, or textures, at different spatial locations.

Following convolutional layers, pooling layers are often used. Pooling reduces 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, which takes the maximum value within a region, and average pooling, which takes the average.

After several convolutional and pooling layers, the extracted features are typically flattened into a one-dimensional vector. This vector is then fed into one or more fully connected layers, similar to those found in traditional neural networks. These layers learn to combine the high-level features extracted by the convolutional layers to perform the final classification or regression task. CNNs have achieved state-of-the-art results in image classification, object detection, and image segmentation.