Which learning method in neural networks is based on optimizing parameters to minimize the difference between predicted and actual outputs?
Understand the Problem
The question is asking about different learning methods in neural networks and specifically which one focuses on optimizing parameters to minimize the difference between predicted and actual outputs.
Answer
backpropagation algorithm
The learning method in neural networks based on optimizing parameters to minimize the difference between predicted and actual outputs is the backpropagation algorithm.
Answer for screen readers
The learning method in neural networks based on optimizing parameters to minimize the difference between predicted and actual outputs is the backpropagation algorithm.
More Information
Backpropagation is crucial for the efficient training of deep neural networks, allow them to adjust weights systematically to improve prediction accuracy.
Tips
A common mistake is confusing backpropagation with gradient descent. While backpropagation calculates the gradient of the loss function concerning each weight, gradient descent updates the weights.
Sources
- Definition of backpropagation algorithm - techtarget.com
- Backpropagation in Neural Network - GeeksforGeeks - geeksforgeeks.org
- Backpropagation - Wikipedia - en.wikipedia.org
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