Train a backpropagation net to classify the letters but not the fonts. One output is assigned to all the fonts of each letter. What is the purpose of training a backpropagation net in this scenario?

1) To classify the fonts of each letter
2) To classify the letters and the fonts separately
3) To classify the letters but not the fonts
4) To classify both the letters and the fonts

Backpropagation net is used to classify letters disregarding fonts, adjusting weights via supervised learning, and is known for being a slow process. Therefore, the correct option is 3).

Explanation:

The purpose of training a backpropagation net in this scenario is to classify the letters but not the fonts. This type of neural network application uses supervised learning to adjust the weights of the network with the aim of recognizing the form of the letters irrespective of the font style.

The back-propagation algorithm begins with small random weights and adjusts these weights based on the error reduction achieved through the gradient descent process on the error landscape. However, it's important to note that because back propagation can be slow, an efficient training set and sufficient training time are crucial for such a task.

The purpose of training a backpropagation net in this scenario is to classify the letters but not the fonts. By assigning one output to all the fonts of each letter, the focus is solely on identifying the letters themselves, regardless of the font style. Backpropagation, a supervised learning algorithm, uses a training set of input/output pairs to adjust the weights of the neural network and minimize the error through gradient descent.