How deeply does Learn Generative AI with PyTorch cover convolutional layer?

Explains sliding-filter arithmetic, tensor shapes, parameters, learned weights, stride, padding, and multi-layer architectures.

Convolutional layer

As explained in Learn Generative AI with PyTorch

A PyTorch convolution layer that applies learned 2D filters to an input tensor to produce feature maps.

A convolutional layer applies learned filters across an input image or tensor to extract spatial patterns and produce feature maps. Unlike a dense layer that connects every input value to every output, it reuses the same filter weights as the filter moves across different areas. section 4.2.1 section 4.5.1

In PyTorch, nn.Conv2d configures the input channels, output channels, kernel size, stride, padding, and optionally bias. Input channels describe the image data; output channels describe how many feature types the layer can extract. section 4.2.1 section 4.5.1

A two-by-two filter over a three-by-three image

Represent a grayscale image as shape (1, 1, 3, 3), then create nn.Conv2d(in_channels=1, out_channels=1, kernel_size=2, stride=1). Setting the layer weights to [[1, 2], [3, 4]] with zero bias produces a 2-by-2 output. Each output value comes from element-wise multiplication of the filter and the current image patch, followed by summation.

Tune the layer by changing these arguments:

Set in_channels to 1 for grayscale input or 3 for RGB input. section 4.2.1
Set out_channels to choose how many learned feature maps the layer produces. section 4.2.1
Increase stride to move the filter farther between positions and downsample the image; use padding to control border handling. section 7.4.1 section 4.5.1

Convolutional layers commonly appear in image classifiers and generative models. A DCGAN discriminator stacks them with batch normalization and LeakyReLU, while a VAE encoder uses them to compress images into latent vectors. Transposed convolutional layers reverse this spatial progression when a decoder or generator reconstructs images. section 4.5.1 section 7.4.1

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Also explained in

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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.
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Math and Architectures of Deep LearningA neural-network layer that applies convolution operations to an input, commonly used to extract local features from images.
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Deep Learning with PyTorch, Second EditionA PyTorch 2D convolution layer module that applies learned filters over spatial input regions.
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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.
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Deep Learning with R, Third EditionA convolutional layer is a neural-network layer that applies learned local filters across spatial or sequential input dimensions.
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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.
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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.
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Optimization Algorithms: AI techniques for design, planning, and control problemsA transformation that applies shared learned weights to neighboring feature representations.
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Sutskever's ListA convolutional layer is a neural-network layer that applies learned local filters across an input to produce spatial feature responses.
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Machine Learning Algorithms in DepthA convolutional layer is a neural-network layer that applies learned convolution kernels to an input representation.
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Build a Text-to-Image Generator (from Scratch)Conv2d is a two-dimensional convolution layer that applies learned spatial filters to image-like tensors.
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Distributed Machine Learning PatternsA convolutional layer is a neural-network layer that applies learned convolutional filters to its input.
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Grokking Machine LearningA convolutional layer is a neural-network layer that produces a filtered representation by applying convolutions across an input.

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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.

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