Chapter 8: RMS Normalization and Residual Connections

Chapter 8 introduces RMSNorm and residual connections as core stabilization techniques for deep neural networks.

RMSNorm rescales vectors by their root mean square to keep activations near unit magnitude.

Residual connections add the input back to the transformed output, creating a shortcut for gradient flow during backpropagation.

Together, these methods help reduce exploding or vanishing activations and make deeper models easier to train reliably.