Enhance Model Performance
This unit explores strategies for improving neural network performance through architecture design and regularization techniques. You will investigate how network depth (number of layers) and width (neurons per layer) affect learning capacity and how regularization methods can be combat overfitting. The regulariztion techniques covered will be L1 and L2 penalties to constrain parameter growth, dropout to randomly deactivates neurons during training, and early stopping to monitor validation performance and halt training at the optimal point. By the end of this unit, you will have developed an intuition for how to choose architectures and regularization strategies to build models that generalize well to unseen data.