2 个仓库
Awesome GitHub RepositoriesSpatial Dimension Controllers
Utilities for modifying output shapes through padding and stride adjustments in convolutional layers.
Distinct from Output Padding Controllers: Distinct from Output Padding Controllers: focuses on the broader spatial dimension control including stride, not just padding.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Spatial Dimension Controllers. Refine with filters or upvote what's useful.
Awesome Spatial Dimension Controllers GitHub Repositories
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- d2l-ai/d2l-en
d2l-ai/d2l-en
29,001在 GitHub 上查看This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
Controls spatial expansion by configuring padding and strides in transposed convolutional operations.
Pythonbookcomputer-visiondata-science - vdumoulin/conv_arithmetic
vdumoulin/conv_arithmetic
14,623在 GitHub 上查看This project provides a collection of visual guides, technical documentation, and animation generation tools designed to explain the mathematical mechanics of neural network layer operations. It serves as an educational resource for understanding the architecture and data mapping processes involved in deep learning. The toolset distinguishes itself by programmatically generating visual representations of standard, transposed, and dilated convolution layers. By utilizing a declarative configuration model, it maps mathematical parameters—such as kernel sizes, strides, and padding—to coordinate-
Calculates output dimensions and spatial relationships for convolution layers based on kernel, stride, and padding parameters.
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