Which of the following are considered regularisation technologies?

The correct choice encompasses several techniques aimed at preventing overfitting during the training of machine learning models. Regularization aims to impose a penalty on the model's complexity, helping it generalize better to unseen data.

Dropout is a specific method used in neural networks where, during training, random neurons are "dropped out" or deactivated, which prevents the network from becoming overly reliant on any single neuron. This forces the model to learn more robust representations and helps in mitigating overfitting. By not allowing certain neurons to participate in every iteration, dropout introduces noise into the training process, leading the model to be more adaptable and better able to generalize.

L1 and L2 regularization are also common regularization techniques that add a penalty to the loss function based on the magnitude of the weights. L1 regularization encourages sparsity in the weights, effectively shrinking some of them to zero, while L2 regularization penalizes the square of the weights’ magnitudes, leading to smaller weights overall. Both contribute to reducing model complexity but differ in their approach.

Momentum optimizer, on the other hand, is a technique used to accelerate the convergence of gradient descent algorithms rather than a regularization method. While it helps improve training speed and stability,