Answer: (iv) All of the above

Explanation — techniques to deal with overfitting in Neural Networks:

1. Dropout (i): Randomly drops neurons (with prob $p\sim0.2-0.5$) during training. Prevents co-adaptation, acts as ensemble approximation. Used in fully connected layers.

2. Regularization (ii):

   • L2 (weight decay): adds $\lambda ||w||_2^2$ to loss, penalizes large weights.
   • L1: adds $\lambda ||w||_1$, induces sparsity.
   • Makes model simpler and generalizes better.

3. Batch Normalization (iii): Normalizes layer inputs per mini-batch to zero mean/unit variance. Reduces internal covariate shift, allows higher learning rates, has slight regularization effect due to noise from batch statistics.

Other methods: Early stopping, data augmentation, reducing network capacity, cross-validation.

Since dropout, L1/L2 regularization and batch norm all help reduce overfitting, (iv) All of the above is correct.