Convolutional neural network

As explained in Graph Neural Networks in Action

A convolutional neural network is a neural network that applies learned convolutional filters to exploit local spatial structure, especially in grid-like data such as images.

A convolutional neural network (CNN) processes grid-like data, especially images, by learning localized patterns. It is commonly used for computer-vision tasks such as image segmentation and classification. section 3.4.1

A typical CNN layer follows a short transformation pipeline:

• Filter or kernel: applies learnable weights that highlight particular features in the input. section 3.4.1
• Activation function: applies a nonlinear transformation to the filter output. section 3.4.1
• Pooling: reduces the output size, producing a more manageable representation for later learning. section 3.4.1

Convolution builds hierarchies of localized patterns. A CNN slides a learned kernel across a fixed pixel grid, whereas a graph convolutional network applies convolution to graph structure to identify node or graph patterns. section 3.4.1

This distinction reflects different inductive biases: CNNs exploit the ordered spatial structure of images, while graph neural networks are designed for permutation-invariant relational data. section 1.2.4

Figure 3.11 A convolution of an input image (left). The kernel (middle) is passed over the image of an animal, resulting in a distinct representation (right) of the input image. In a deep learning process, the parameters of the filter (the numbers in the matrix) are learned parameters.

The image-and-kernel illustration shows how learned filter weights pass over an animal image to produce a distinct feature representation. section 3.4.1

How Graph Neural Networks in Action covers it

How deep it goes

Explains the filter–activation–pooling pipeline and contrasts grid convolution with graph convolution.

Examples

Uses an animal image and a sliding kernel to illustrate feature extraction, alongside image segmentation and classification.

What you can do after

Evaluate CNN layers as sequences of learned filtering, nonlinear activation, and spatial reduction.

How it connects

Connects convolution with kernels, activation functions, pooling, graph neural networks, and graph convolutional networks.

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Related concepts

• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• convolutional layeruses · A convolutional layer is a neural-network layer that applies shared learned filters to local regions of structured input to produce feature representations.
• poolinguses · Pooling is an operation that aggregates values or representations across regions or axes to produce a reduced or summarized representation.
• convolution kerneluses · A convolution kernel is a small array or tensor of weights applied across local input regions to compute weighted responses and detect or transform patterns.
• U-Netincludes · U-Net is an encoder-decoder neural-network architecture with downsampling and upsampling paths connected by skip connections for combining features at multiple spatial scales.
• LeNetincludes · LeNet is an early convolutional neural-network architecture designed primarily for handwritten-digit and character recognition.

This explainer was generated by Manning's knowledge graph from the text of Graph Neural Networks in Action. The section links point to the parts of the book it draws on.