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Convolution is the basic building operator of Convolutional Neural Networks (CNNs). In this chapter, we will have a brief view on the emergence of convolution layers, especially dedicated to processing multi-channel (e.g. RGB) input images. Then, we use convolution layers to build Convolutional Neural Networks that operate on real-world images. Following the design of classic LeNet-5, we provide the basic principles in constructing a Convolutional Neural Network for handwritten digit recognition.

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Convolution and CNN Building Blocks

  • 章节

  • 发表日期：

    2026年

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  • Yiran Chen,
  • Hai Li,
  • Huanrui Yang

摘要

Convolution is the basic building operator of Convolutional Neural Networks (CNNs). In this chapter, we will have a brief view on the emergence of convolution layers, especially dedicated to processing multi-channel (e.g. RGB) input images. Then, we use convolution layers to build Convolutional Neural Networks that operate on real-world images. Following the design of classic LeNet-5, we provide the basic principles in constructing a Convolutional Neural Network for handwritten digit recognition.