During the training process of an artificial neural network, which method is typically used to update the network's parameters?
Understand the Problem
The question is asking about methods used for updating the parameters of an artificial neural network during its training process. It is trying to assess knowledge regarding common techniques, specifically highlighting the method that is most frequently employed in practice.
Answer
Gradient descent.
The final answer is gradient descent.
Answer for screen readers
The final answer is gradient descent.
More Information
Gradient descent is an optimization algorithm used to minimize a function by iteratively adjusting the parameters in the direction that reduces the loss function. It's key to efficiently training neural networks by calculating the gradient of the loss function with respect to the network's parameters.
Tips
A common mistake is thinking backpropagation is the method used. While backpropagation calculates gradients, gradient descent is the actual optimization process used to update the parameters.
Sources
- Neural Network Training - an overview | ScienceDirect Topics - sciencedirect.com
- Neural network (machine learning) - Wikipedia - en.wikipedia.org
- 5 algorithms to train a neural network - neuraldesigner.com
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