=================================================================================

When working with small datasets, simpler models (e.g. page3300) are often sufficient because complex models can overfit, meaning they learn the noise in the training data rather than the underlying patterns. This can lead to poor performance when the model is used on new, unseen data.

As the size of the dataset increases, it often becomes feasible to use more complex models. This is because larger datasets can provide more information and support the learning of more complex patterns without overfitting. Additionally, with more data, the likelihood of encountering overfitting decreases, which allows for the exploration of deeper or more intricate architectures.

Incorporating techniques like dropout is particularly useful in complex neural networks. Dropout helps in preventing overfitting by randomly setting the output features of a number of neurons to zero during the training process. This ensures that the model does not rely too heavily on any single neuron, thus promoting a more generalized model that performs better on new, unseen data.

Therefore, the choice of model complexity and regularization techniques like dropout should be guided by the size and nature of your dataset, as well as the specific task you are trying to accomplish.

===========================================