What is true regarding the neural network family of algorithms?

I Neural Networks can have at most two hidden layers.
II Neural Network training involves the computation of several partial derivatives of the error function concerning the model parameters.
III Neural Networks can be used as a supervised and an unsupervised method.
IV Neural Networks can be used only as a supervised method.

Question

What is true regarding the neural network family of algorithms?

I	Neural Networks can have at most two hidden layers.
II	Neural Network training involves the computation of several partial derivatives of the error function concerning the model parameters. 
III	Neural Networks can be used as a supervised and an unsupervised method.
IV	Neural Networks can be used only as a supervised method.

BlackTom AI · Accepted Answer

When evaluating statements about neural network family algorithms, I will assess each claim on its own merits.
Option I: 'Neural Networks can have at most two hidden layers.' This is incorrect. There is no general upper bound of two hidden layers in neural networks; architectures can have many hidden layers, which is exactly what deep learning exploits. The claim incorrectly constrains model capacity and depth.
Option II: 'Neural Network training involves the computation of several partial derivatives of the error function concerning the model parameters.' This is correct. Training typically relies on gradient-based optimization, where we compute partial derivatives of the loss (error) with respect to weights and biases, most commonly via backpropagation, to update parameters.
Option III: 'Neural Networks can be used as a supervised and an unsupervised method.' This is true. While many NN applications are supervised (e.g., classification, regression), neural networks are also used in unsupervised or self-supervised contexts (e.g., autoencoders, restricted Boltzmann machines, clustering-inspired neural approaches, and representation learning without labeled data).
Option IV: 'Neural Networks can be used only as a supervised method.' This is false. The same network architectures can be repurposed for unsupervised or semi-supervised tasks, given appropriate training objectives and data.
In summary, the statements II and III accurately describe capabilities and training aspects of neural networks, while I and IV misstate architectural limits and usage. The combination 'II and III' aligns with the true properties discussed above.

FINTECH 540.01.Fa25 Final Exam

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