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In a neural network, what if all the weights are initialized with the same value?

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In simplest terms, if all the neurons have the same value of weights, each hidden unit will get exactly the same signal. While this might work during forward propagation, the derivative of the cost function during backward propagation would be the same every time.

In short, there is no learning happening by the network! What do you call the phenomenon of the model being unable to learn any patterns from the data? Yes, underfitting.

Therefore, if all weights have the same initial value, this would lead to underfitting.

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