Convolutional neural network

As explained in Machine Learning Algorithms in Depth

A convolutional neural network is a neural-network architecture that uses convolution and pooling operations to process structured spatial or grid-like data.

A convolutional neural network (CNN) processes structured data with trainable filters, convolution, nonlinear activation, and often pooling. For an image of width W, height H, and C channels, a kernel slides across positions, computes dot products, and forms feature maps. Shared filters reduce parameters and support translation-invariant classification. section 10.2

A typical CNN proceeds through these operations:

• Convolve input data with trainable kernels to extract features. section 10.2
• Apply nonlinear activation functions between stacked convolutional layers. section 10.2
• Down-sample feature maps with max pooling, average pooling, or global average pooling. section 10.2
• Flatten the resulting representation and pass it to dense and softmax layers for classification. section 10.2.1

LeNet for handwritten digits

A LeNet-style model classifies MNIST digits by stacking convolution, ReLU, and max-pooling stages, increasing filter counts while reducing feature-map dimensions. A final dense softmax classifier produces predictions. Keras/TensorFlow code loads and reshapes MNIST data, trains the model, evaluates test accuracy, records loss and accuracy, and uses early stopping; the reported test accuracy reaches 99%. section 10.2.1

Figure 10.4 LeNet architecture for MNIST digits classification

Use the architecture figure to trace the progression from convolution and activation through pooling to classification. section 10.2.1

How Machine Learning Algorithms in Depth covers it

How deep it goes

Derives convolution and pooling operations, then applies them in a LeNet workflow; broader architecture internals are not developed.

Examples

Demonstrates LeNet on MNIST handwritten digits and names ResNet50 image search as an advanced application.

What you can do after

Apply CNNs via Keras/TensorFlow to train, evaluate, and visualize an MNIST classifier.

How it connects

Connects convolution with pooling, activation functions, dense layers, softmax classification, MNIST, and ResNet50.

liveBookmanning.com

Read Machine Learning Algorithms in Depth on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Natural Language Processing in Action, Second EditionA convolutional neural network is a neural network that applies learned convolutional filters to structured inputs to detect local patterns and construct hierarchical representations.
• 

  liveBookmanning.com
  Sutskever's ListA convolutional neural network is a neural network architecture that applies learned convolutional filters to structured input such as images.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionA neural network built from convolution and pooling layers that processes data with local spatial structure.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Deep Learning with PyTorch, Second EditionA neural network that uses convolutional layers to process spatial data.
• 

  liveBookmanning.com
  Distributed Machine Learning PatternsA convolutional neural network is a neural network model that uses convolutional operations to learn spatial or locally structured representations.
• 

  liveBookmanning.com
  Inside Deep Learning: Math, Algorithms, ModelsA neural-network design that uses convolutional layers to exploit spatial structure in image data.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Learn Generative AI with PyTorchA neural network architecture using convolution operations, here applied to multidimensional music data to extract spatial features.
• 

  liveBookmanning.com
  The AI Pocket BookA neural network architecture that applies convolutions and downsampling to learn spatial features from grid-like data such as images.
• 

  liveBookmanning.com
  Data Without LabelsA convolutional neural network is a neural-network model that uses convolutional operations to learn spatial or locally structured features.
• 

  liveBookmanning.com
  Math and Architectures of Deep LearningA neural network that uses convolutional layers to process structured grid data such as images.
• 

  liveBookmanning.com
  Grokking Machine LearningA neural-network architecture that uses convolutional operations to learn localized and hierarchical patterns, especially in structured data such as images.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Inside AIA convolutional neural network is a neural-network model that applies learned convolutional filters to structured data such as images.

Compare how 18 books explain convolutional neural network →

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 Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.