Convolutional layer

As explained in Machine Learning Algorithms in Depth

A convolutional layer is a neural-network layer that applies learned convolution kernels to an input representation.

A convolutional layer applies learned filters, or kernels, across an input representation to produce feature maps. For an image of width W, height H, and C channels, a two-dimensional convolution evaluates a dot product at each possible kernel position. Convolutional layers reduce parameter counts and help preserve translation invariance. section 10.2

A convolutional operation proceeds by:

• Sliding a kernel across the input matrix. section 10.2
• Computing a dot product between the kernel and the input values at each position. section 10.2
• Collecting the resulting scalar values into a feature map. section 10.2
• Applying nonlinear activation functions between stacked convolutional layers. section 10.2

From handwritten digits to sequence similarity

A LeNet-style MNIST classifier stacks convolution, ReLU, and max-pooling operations, increases the number of filters, flattens the final feature map, and finishes with a dense softmax classifier. For question-pair similarity, Conv1D and max-pooling process each embedded question branch before concatenation and dense classification. section 10.2.1 section 10.3.2

(10.8)

Use the convolution figure to inspect how kernel positions generate feature-map values. section 10.2

How Machine Learning Algorithms in Depth covers it

How deep it goes

Defines sliding kernels, dot products, feature maps, stacking, and activation placement, but does not derive gradient formulas.

Examples

Demonstrates 2D image convolution in LeNet for MNIST and 1D convolution in a multi-input question-similarity model.

What you can do after

Builds CNNs with Conv2D or Conv1D, pooling, nonlinearities, and downstream classification layers in Keras/TensorFlow.

How it connects

Connects convolutional layers with pooling, ReLU, dense layers, softmax classifiers, feature maps, and translation invariance.

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 layer is a neural-network layer that applies learned filters across local regions of its input to produce feature maps.
• 

  liveBookmanning.com
  Math and Architectures of Deep LearningA neural-network layer that applies convolution operations to an input, commonly used to extract local features from images.
• 

  liveBookmanning.com
  Deep Learning with PyTorch, Second EditionA PyTorch 2D convolution layer module that applies learned filters over spatial input regions.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionA two-dimensional convolution layer used here with a 1×1 kernel to project a residual to a matching shape.
• 

  liveBookmanning.com
  Deep Learning with R, Third EditionA convolutional layer is a neural-network layer that applies learned local filters across spatial or sequential input dimensions.
• 

  liveBookmanning.com
  Grokking AI Algorithms, Second EditionA convolutional layer is a neural-network layer that applies shared filters to local regions of an input representation while preserving spatial organization.
• 

  liveBookmanning.com
  Evolutionary Deep Learning: Genetic algorithms and neural networksA convolutional layer applies learned local filters across structured input locations to detect spatial or otherwise local patterns.
• 

  liveBookmanning.com
  Optimization Algorithms: AI techniques for design, planning, and control problemsA transformation that applies shared learned weights to neighboring feature representations.
• 

  liveBookmanning.com
  Learn Generative AI with PyTorchA PyTorch convolution layer that applies learned 2D filters to an input tensor to produce feature maps.
• 

  liveBookmanning.com
  Sutskever's ListA convolutional layer is a neural-network layer that applies learned local filters across an input to produce spatial feature responses.
• 

  liveBookmanning.com
  Build a Text-to-Image Generator (from Scratch)Conv2d is a two-dimensional convolution layer that applies learned spatial filters to image-like tensors.
• 

  liveBookmanning.com
  Distributed Machine Learning PatternsA convolutional layer is a neural-network layer that applies learned convolutional filters to its input.
• 

  liveBookmanning.com
  Grokking Machine LearningA convolutional layer is a neural-network layer that produces a filtered representation by applying convolutions across an input.

Compare how 14 books explain convolutional layer →

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.
• 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.
• downsamplingused by · Downsampling is the reduction of data quantity, spatial resolution, temporal resolution, or sampling rate by retaining, selecting, or aggregating fewer representative values or observations.
• 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.
• batch sizeuses · Batch size is the number of items grouped together for processing or computation as a single batch.
• encoderused by · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.

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.