What is the difference between dropout and batch normalization?

Dropout and Batch Normalization are two different methods used in neural networks to improve training, but with different goals and mechanisms.

• Dropout is a regularization technique that randomly "turns off" (zeros out) some neurons during training. This helps prevent overfitting by making the network not rely on specific neurons.

• Batch Normalization is a method of normalizing the inputs of each layer across a mini-batch, which stabilizes and accelerates training, reducing the problem of internal covariate shift.

Key differences:

• Dropout reduces overfitting by introducing stochasticity.
• Batch Normalization speeds up and stabilizes training, improving convergence.

They can usually be used together, but they serve different functions.