Convolution kernel

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

A convolutional filter is a learned array of weights that is slid across local input regions to produce feature responses.

A convolution kernel is a learned array of weights that slides across local image patches, producing one feature-response value per position. section 7.1

A simplified convolution workflow is:

• Pass the kernel across the image with a stride, typically one pixel by one pixel. section 7.1
• Apply the kernel to each local patch to create an output value and assemble those values into an output patch. section 7.1
• Stack convolutional layers with different filter counts, then use pooling to summarize their increasingly large outputs. section 7.1

Keras convolution stack

A practical model starts with Conv2D(64, (3, 3)) for a 28×28×1 image, followed by max pooling, then 32-filter and 16-filter convolutional layers. The resulting model has 23,728 trainable parameters.

Figure 7.3 Demonstration of the convolutional filter operation

Inspect the visual demonstration of a kernel sliding across an image patch and producing an output value. section 7.1

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes

Explains sliding, stride, learned weights, channel changes, and pooling, but avoids mathematical derivation.

Examples

Demonstrates Fashion-MNIST image patches, a visual filter operation, and a three-layer Keras Conv2D stack.

What it assumes

Assumes basic image tensors and channels, while demonstrating how to reshape grayscale images into channel-aware inputs.

What you can do after

Build convolutional image layers with Keras Conv2D and inspect their output shapes and parameter counts.

How it connects

Connects convolution kernels with convolutional neural networks, Conv2D layers, stride, channels, feature maps, and max pooling.

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Also explained in

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  Natural Language Processing in Action, Second EditionA convolutional kernel is a parameter matrix or tensor whose sliding weighted operation detects a local pattern in an input sequence.
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  Math and Architectures of Deep LearningA learned kernel applied in a convolution layer to detect local spatial patterns in images.
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  Inside Deep Learning: Math, Algorithms, ModelsA learned array of weights applied over local input regions to compute convolution outputs.
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  Deep Learning with PyTorch, Second EditionA small learned filter window applied over local regions of an input to compute weighted sums.
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  Grokking AI Algorithms, Second EditionA convolutional filter is a small learned grid of weights that detects a particular local pattern in image data.
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  Deep Learning with Python, Third EditionThe learned weight tensor used by a convolution to transform local input patches.
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  Deep Learning with R, Third EditionA convolution kernel is a learned weight matrix that transforms each extracted input patch into an output feature vector.
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  Graph Neural Networks in ActionA learnable weighted operation that transforms input data to emphasize particular patterns or features.
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  Learn Generative AI with PyTorchA learned convolutional kernel whose weights slide across local input regions to produce feature responses.
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  Sutskever's ListA convolutional kernel is a grid of learned weights applied across local regions of an input to produce spatial responses.

Compare how 11 books explain convolution kernel →

Related concepts

• convolutional neural networkused by · A convolutional neural network is a neural-network architecture that applies learned convolutional operations to extract patterns from structured data.
• convolutional layerused by · A convolutional layer is a neural-network layer that applies shared learned filters to local regions of structured input to produce feature representations.
• feature mapproduces · A feature map is a spatially organized array of neural-network activations representing the presence or strength of learned features at different locations.
• image processinguses · Image processing is the computational manipulation and analysis of digital image data to transform images or extract information.

This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.