Conclusion

Convolution is a way to use local structure.

You have seen the main ideas:

• local connectivity reads neighborhoods
• kernels slide across positions
• feature maps record detector responses
• padding and stride control shape
• channels carry parallel grids of values
• receptive fields grow with depth
• parameter sharing reduces weights
• pooling summarizes local regions
• convolution fits data with meaningful local neighborhoods

The next chapter studies skip, residual, and gated connections. Those ideas explain how deeper networks preserve information and gradients while stacking many transformations.