Convolutional neural network

As explained in Delivering Machine Learning Projects: From design to deployment

A convolutional network is a neural network architecture that applies local filters over structured inputs to capture spatial or local patterns.

A convolutional neural network is a neural network architecture for image recognition that preserves and exploits relationships among nearby pixels. It applies learned filters locally instead of treating each pixel as an isolated input. section 7.3.4

Its inductive bias comes from three linked operations:

• Slides a learned filter across the input in a moving window. section 7.3.4
• Propagates signals locally, normalizing and filtering activations at each locality. section 7.3.4
• Pools information between layers to combine local patterns into higher-level representations. section 7.3.4

Why locality matters

A perceptron receiving an image one pixel at a time loses the spatial order and struggles with object recognition. A convolutional network retains neighboring-pixel context through local filtering. section 7.3.4

Figure 7.6 Inductive bias in ML algorithms (adapted from Jumper, Evans, et. al. [6]. The sequence (a) through (e) is determined by the order in which these biases gained popularity in the ML community. The arrows indicate the propagation of signal across a network or in a graph.

Compare convolutional networks with other inductive biases and network structures. section 7.3.4

How Delivering Machine Learning Projects: From design to deployment covers it

How deep it goes

Explains local filtering, sliding windows, normalization, and pooling, but stops short of mathematical or implementation detail.

Examples

Contrasts pixel-by-pixel perceptron processing with learned filters sliding across neighboring image pixels.

What you can do after

Evaluate why local filtering is appropriate for image-recognition problems.

How it connects

Connects inductive bias with neural networks, perceptrons, image recognition, and alternative architectures such as transformers.

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  Deep Learning with PyTorch, Second EditionA neural network that uses convolutional layers to process spatial data.
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  Learn Generative AI with PyTorchA neural network architecture using convolution operations, here applied to multidimensional music data to extract spatial features.
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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 Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.