Convolutional neural network

As explained in Grokking AI Algorithms, Second Edition

A convolutional neural network is a neural-network architecture that applies learned convolutional filters and typically pooling operations to detect spatial patterns in structured data such as images.

A convolutional neural network (CNN) analyzes nearby parts of structured data with sliding, learned filters. It preserves spatial relationships instead of treating every input as an unrelated list of values, making it especially useful for images. section 9.29 section 12.13

A CNN processes an image in two especially important ways:

• Convolution examines a pixel together with neighboring pixels, using filters to detect features such as edges and other visual patterns. section 9.29
• Pooling downsamples the detected patterns by summarizing features, encoding distinctive visual signatures more concisely. section 9.29
• Successive layers can detect increasingly complex features, moving from simple edges toward objects such as faces and cars. section 12.13

Following a visual pattern

Imagine an image containing a recognizable object. A filter slides across local pixel regions and responds to useful patterns, such as edges. Pooling summarizes those responses, while later layers combine the simpler patterns into a distinctive object signature. The resulting representation can support image classification. section 9.29 section 12.13

CNNs support image classification, optical character recognition, and medical-image analysis. They can also detect local word patterns for text classification, although they are less suitable for complex long-range sentence meaning. Unlike ordinary linear feed-forward layers, convolutional layers analyze regions independently while retaining pixel positions. section 9.29 section 12.13 section 11.11

Figure 9.33 Simple example of a CNN

Use the diagram to trace how convolution and pooling turn image patterns into a concise learned representation. section 9.29

Figure 12.23 The difference between an ANN and a CNN

Use the comparison to see how a convolutional layer preserves spatial structure while a linear layer processes inputs uniformly. section 12.13

How Grokking AI Algorithms, Second Edition covers it

How deep it goes

Explains local convolution, pooling, feature hierarchies, and spatial structure without detailing filter-learning mathematics or implementation code.

Examples

Uses pixels, neighboring regions, edges, faces, cars, X-rays, body scans, and local word patterns as examples.

What you can do after

Evaluate when spatial feature detection suits image classification, OCR, medical scans, or local-pattern text classification.

How it connects

Connects convolution and pooling with image classification, optical character recognition, U-Net, diffusion, and text classification.

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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 Grokking AI Algorithms, Second Edition. The section links point to the parts of the book it draws on.