Convolutional neural network

As explained in Math and Architectures of Deep Learning

A neural network that uses convolutional layers to process structured grid data such as images.

A convolutional neural network processes structured grids such as images by learning local patterns and combining them into increasingly complex representations. Unlike a fully connected layer, each output uses a small neighborhood of inputs, while shared weights slide across the whole image. This reduces parameters and applies the same feature detector everywhere. section 10.0

The hierarchy moves from simple visual properties to larger structures: lower layers can detect edges and corners, later layers can combine them into ears, eyes, and noses, and still higher layers can recognize faces. Pooling summarizes local patches and downsamples feature maps, making small positional changes less consequential. section 10.0 section 10.7

From local patches to image understanding

A convolutional filter can detect an edge throughout an image because its weights are shared. A later layer combines detected edges into parts such as eyes or noses, while pooling with a 2 × 2 kernel and stride 2 reduces each feature map to half its width and height. These representations support image classification and, when locations are also predicted with bounding boxes, object detection. section 10.0 section 10.7 section 11.0

Figure 10.19 Max pooling using a 2 × 2 kernel with stride 2. The resulting output feature map is half the size of the input feature map. Each value of the output feature map is a max of the corresponding local patch in the input feature map.

A pooling diagram shows how each 2 × 2 input patch becomes one maximum value and how the output feature map is reduced. section 10.7

How Math and Architectures of Deep Learning covers it

How deep it goes

Explains local connections, shared weights, hierarchical features, pooling, downsampling, and output-size effects.

Examples

Uses edge, corner, ear, eye, nose, and face detectors, plus 2 × 2 max- and average-pooling examples.

What you can do after

Understand how local filters and pooling build image representations for classification and object detection.

How it connects

Connects convolution to fully connected layers, pooling, feature maps, image classification, object detection, and bounding boxes.

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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 Math and Architectures of Deep Learning. The section links point to the parts of the book it draws on.