Improving Deep Learning Models

Step beyond the basics of building deep learning models and dive into the secrets that make them truly powerful. In this episode, we unravel the art of pushing models past their limits—exploring how techniques like L2 regularization and dropout keep networks sharp without overfitting, and how batch normalization speeds up training while keeping it stable. We’ll also uncover the magic of data augmentation for richer datasets, and the smart shortcuts of transfer learning and fine-tuning that let you harness pre-trained models to tackle new challenges with ease.