convolution layer
[/ˌkɑːnvəˈluːʃən ˈleɪər/]
nounpl: convolution layers
camada de convolução
1. A fundamental building block in convolutional neural networks (CNNs) that applies convolution operations to input data, using filters to detect features such as edges, textures, or patterns
The first convolution layer of the neural network extracts low-level features from the input image.
A primeira camada de convolução da rede neural extrai características de baixo nível da imagem de entrada.
2. The mathematical operation where a filter (kernel) slides across the input to produce feature maps through element-wise multiplication and summation
Each convolution layer applies multiple filters to generate different feature maps.
Cada camada de convolução aplica múltiplos filtros para gerar diferentes mapas de características.
This is highly specialized technical terminology used exclusively in machine learning, artificial intelligence, and computer vision fields. The term is used identically in both American English and Brazilian Portuguese technical communities, with minimal regional variation. It is primarily encountered in academic papers, programming documentation, and professional AI/ML discussions rather than in everyday language.
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