: The paper "Going Deeper with Convolutions" introduced the Inception architecture, which significantly advanced deep learning by increasing network depth while managing computational cost.
If you are referring to the seminal textbook by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Chapter 7 focuses on Regularization for Deep Learning . Key concepts in this chapter include: Parameter Norm Penalties : Techniques like L1cap L to the first power L2cap L squared regularization ( weightdecayw e i g h t d e c a y ) to limit model capacity. 7 of 1
: Training on examples that have been intentionally perturbed to fool the model. 2. Chapter 7 of the "Neural Networks" Series (3Blue1Brown) : The paper "Going Deeper with Convolutions" introduced