During training, a special layer randomly turns off some of the connections in the network. What is this layer called, and what does it help prevent?

This layer is called Dropout. It helps prevent overfitting.

Dropout works by randomly setting a fraction of the outputs of neurons in a layer to zero during each training step. When a neuron's output is set to zero, it means that neuron, along with its incoming and outgoing connections, is temporarily removed from the network for that particular training example. This random deactivation happens independently for each neuron and for each training iteration.

Overfitting occurs when a neural network learns the training data too specifically, including noise or irrelevant details, rather than learning the general patterns. This leads to the network performing very well on the data it was trained on but poorly on new, unseen data. It's like memorizing answers to a test without understanding the subject, so you fail questions outside the memorized list.

Dropout prevents overfitting by forcing the network to learn more robust and general features. Because neurons are randomly dropped, no single neuron can rely too heavily on the presence of other specific neurons or connections. This encourages the network to develop more independent and diverse internal representations. It effectively means that the network is less likely to memorize specific training examples and more likely to generalize well to new data, improving its performance on tasks it hasn't seen before.