Managing the bias–variance trade-off is essential for building deep learning models that generalize well. This chapter first defines bias (underfitting) and variance (overfitting) and illustrates their interplay using decision-boundary examples and training/validation error scenarios. It then introduces L2 regularization—adding a weight-penalty term to the cost function—and derives the modified forward/backward propagation equations to shrink model weights and reduce complexity. The chapter applies L2 regularization to a real-world network-intrusion detection task (KDD Cup 1999), demonstrating how increasing the regularization strength drives weights toward zero and improves test-set performance. Next, the dropout technique is presented: during training, neurons are randomly masked out and activations rescaled (“inverted dropout”) to prevent co-adaptation. A hands-on implementation demonstrates how to integrate dropout masks into both forward and backward passes, and an image-classification case study (horses vs. humans) highlights dropout’s effect on training dynamics and generalization. Finally, early stopping is discussed as a practical strategy to halt training when validation loss ceases to improve, balancing bias and variance without altering the model architecture. By combining these regularization methods—L2 penalties, dropout, and early stopping—readers will be able to build robust deep networks that maintain high accuracy on unseen data.