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Module 2 of 2

Convolutional Neural Networks & Computer Vision

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1. Convolution Operations and Feature Maps+100 XP
Module 2/2 · Lesson 1/1
Convolution Operations and Feature Maps
deep-learning
intermediate
+100 XP

Convolution Operations

A convolution operation slides a filter (kernel) over an input feature map, computing dot products to produce output feature maps.

Key Parameters

  • Kernel size: Spatial extent of the filter (3×3 most common)
  • Stride: Step size when sliding (1 = every pixel, 2 = skip)
  • Padding: Zeros added around input to control output size
  • Channels: Number of filters = number of output channels

Output Size Formula

Hout=Hin+2PKS+1H_{out} = \left\lfloor\frac{H_{in} + 2P - K}{S}\right\rfloor + 1