The loss or error value in neural networks is generated during the Backward propagation phase. Here's a step-by-step explanation:

1. Forward Propagation: In this phase, the neural network makes its predictions by passing the input data through the layers of neurons. The output is the predicted value.

2. Calculation of Loss: After the forward propagation, the predicted output is compared with the actual output. The difference between the predicted and actual output is calculated as the loss or error. This is usually done using a loss function.

3. Backward Propagation: In this phase, the error calculated is propagated back through the network. This is done to adjust the weights and biases of the neurons in order to minimize the error. The error value is used to calculate the gradient of the loss function, which is then used to update the weights and biases.

So, the loss value is generated during the Backward Propagation phase (Option B).

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The loss or error value in neural networks is generated during the Backward propagation phase. Here's a step-by-step explanation:

1. Forward Propagation: In this phase, the neural network makes its predictions by passing the input data through the layers of neurons. The output is the predicted value.

2. Calculation of Loss: After the forward propagation, the predicted output is compared with the actual output. The difference between the predicted and actual output is calculated as the loss or error. This is usually done using a loss function.

3. Backward Propagation: In this phase, the error calculated is propagated back through the network. This is done to adjust the weights and biases of the neurons in order to minimize the error. The error value is used to calculate the gradient of the loss function, which is then used to update the weights and biases.

So, the loss value is generated during the Backward Propagation phase (Option B).

Knowee AI · Accepted Answer

The loss or error value in neural networks is generated during the Backward propagation phase. Here's a step-by-step explanation:

1. Forward Propagation: In this phase, the neural network makes its predictions by passing the input data through the layers of neurons. The output is the predicted value.

2. Calculation of Loss: After the forward propagation, the predicted output is compared with the actual output. The difference between the predicted and actual output is calculated as the loss or error. This is usually done using a loss function.

3. Backward Propagation: In this phase, the error calculated is propagated back through the network. This is done to adjust the weights and biases of the neurons in order to minimize the error. The error value is used to calculate the gradient of the loss function, which is then used to update the weights and biases.

So, the loss value is generated during the Backward Propagation phase (Option B).

Which of the following phases in neural networks does the LOSS (or error) value generate from?AForward propagationBBackward propagationCInput observation feedsDNone of the above

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