Which statement is true regarding neural networks and hidden layers?

Neural networks are designed to mimic the way the human brain processes information, and they leverage layers of interconnected nodes (neurons) to learn patterns in data. Hidden layers play a crucial role in this process, especially when dealing with complex problems.

The presence of hidden layers allows the neural network to develop more sophisticated representations of the data. These layers enable the model to capture intricate patterns and relationships that may not be apparent with a single layer. This complexity is essential for tasks such as image and speech recognition, where the data exhibits a high degree of variability and non-linearity.

By adding more hidden layers, the model can improve its ability to generalize from the training data to unseen data, leading to increased accuracy. This capability is particularly important for complex problems that require a nuanced understanding of the input data. In contrast, using fewer layers might suffice for simpler problems but may lead to underfitting in more complex scenarios.

Thus, the assertion that hidden layers are necessary for increased accuracy in complex problems is valid, as they enhance the network's ability to learn from and adapt to intricate data structures.