I'm pretty sure C and D are also hyperparameters. Biases and weights are part of the model's architecture and can be adjusted during the training process.
Hmm, I think A and B are the correct hyperparameters. The number of hidden layers and nodes in each layer are crucial hyperparameters that can be tuned to optimize the model's performance.
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