Convolutional neural network

As explained in Data Without Labels

A convolutional neural network is a neural-network model that uses convolutional operations to learn spatial or locally structured features.

A convolutional neural network (CNN) is a deep-learning model primarily used for image and video processing. It learns patterns directly from raw images, making it useful for computer-vision tasks such as classification, detection, and recognition. section 8.6 section 8.1.1

A typical CNN processes an image through these stages:

• Represent the image as a three-dimensional tensor containing width, height, and RGB channels. section 8.6.1
• Slide small filters, commonly 3 × 3 or 5 × 5, across the image; element-wise multiplication and summation produce feature maps for lines, edges, curves, or textures. section 8.6.1
• Apply ReLU to introduce nonlinearity, then use pooling—often max pooling—to reduce spatial dimensions, computation, and overfitting. section 8.6.1
• Flatten the resulting maps and pass them to a fully connected layer for high-level image classification. section 8.6.1
• Use an output function such as softmax to assign probabilities to classes. section 8.6.1

Classifying an image

For a cat-versus-dog classifier, convolutional filters extract visual features, later layers combine them for classification, and softmax reports the probability of each class. Applying the same filter across image regions reduces the number of parameters compared with a traditional neural network. section 8.6.1

Figure 8.13 CNN process. The original data is 6 × 6, and the filter applied is 3 × 3, which results in a 4 × 4 output.

Inspect the 6 × 6 input and 3 × 3 filter example to see how convolution produces a 4 × 4 output. section 8.6.1

How Data Without Labels covers it

How deep it goes

Explains tensors, filters, feature maps, activations, pooling, classification, and shared parameters, but provides only a brief overview.

Examples

Uses cat-versus-dog classification, license-plate reading, cancer-cell detection, bone-fracture detection, and facial recognition.

What you can do after

Apply convolutional architectures to image classification, image processing, object detection, and related recognition problems.

How it connects

Connects deep learning with convolution layers, ReLU, pooling, fully connected layers, softmax, computer vision, and recurrent neural networks.

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Also explained in

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  Deep Learning with R, Third EditionA convolutional neural network is a neural network that applies convolution-based operations to learn spatial or locally structured features, especially from grid-like data such as images.
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  Deep Learning with Python, Third EditionA neural network built from convolution and pooling layers that processes data with local spatial structure.
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  Evolutionary Deep Learning: Genetic algorithms and neural networksA convolutional neural network is a neural network that processes structured inputs with learned convolutional filters, typically for tasks such as image classification.
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  Deep Learning with PyTorch, Second EditionA neural network that uses convolutional layers to process spatial data.
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  Distributed Machine Learning PatternsA convolutional neural network is a neural network model that uses convolutional operations to learn spatial or locally structured representations.
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  Inside Deep Learning: Math, Algorithms, ModelsA neural-network design that uses convolutional layers to exploit spatial structure in image data.
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  Graph Neural Networks in ActionA 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.
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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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  The AI Pocket BookA neural network architecture that applies convolutions and downsampling to learn spatial features from grid-like data such as images.
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  Math and Architectures of Deep LearningA neural network that uses convolutional layers to process structured grid data such as images.
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  Grokking Machine LearningA neural-network architecture that uses convolutional operations to learn localized and hierarchical patterns, especially in structured data such as images.
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  Grokking AI Algorithms, Second EditionA 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.
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  Delivering Machine Learning Projects: From design to deploymentA convolutional network is a neural network architecture that applies local filters over structured inputs to capture spatial or local patterns.
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  Machine Learning Algorithms in DepthA convolutional neural network is a neural-network architecture that uses convolution and pooling operations to process structured spatial or grid-like data.
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  Inside AIA convolutional neural network is a neural-network model that applies learned convolutional filters to structured data such as images.

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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 Data Without Labels. The section links point to the parts of the book it draws on.