Forward and Backward Propagation

Forward Propagation

Definition

Forward propagation is the process where information moves from the input layer to the output layer of a neural network. Each neuron processes its inputs using weights and an activation function, passes its output forward, and once the final layer is reached, the network produces a prediction.

Backward Propagation

After a neural network makes a prediction through forward propagation, its output is compared to the actual data to calculate the error.

Definition

Backward propagation, or backpropagation, is the process of using this error to move backward through the network and adjust the neuron weights.

By updating the weights in this way, the network gradually reduces its error and improves the accuracy of its predictions.

Note

The neural network error can be calculated in different ways depending on the task, but it is always a floating point number.

Neural networks learn by repeating forward and backward propagation many times. With each iteration, the model improves, but it never reaches “perfect accuracy.” Training ends when performance becomes acceptable or when the model stops improving after many iterations.

1. What is forward propagation in a neural network?

2. What is backpropagation in a neural network?

3. When training a neural network, what happens after forward propagation stage?

What is forward propagation in a neural network?

Select the correct answer

This is the process of training a neural network.

This is the process by which information passes through the network from the input layer to the output layer.

This is the process in which the neural network handles the error after the prediction.

This is the process of setting the initial weights on the network.

What is backpropagation in a neural network?

Select the correct answer

This is the process in which the neural network calculates the error after a prediction and uses this information to adjust the weights.

This is the process of setting the initial weights on the network.

This is the process by which information passes through the network from the input layer to the output layer.

This is not a part of the neural network training process.

When training a neural network, what happens after forward propagation stage?

Select the correct answer

The neural network starts a new forward propagation cycle.

The forecast is compared with real data and the error is calculated.

The weights in the network are adjusted regardless of the error.

The network stops running because the learning process is complete.

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 7

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Forward and Backward Propagation

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Forward Propagation

Definition

Backward Propagation

After a neural network makes a prediction through forward propagation, its output is compared to the actual data to calculate the error.

Definition

Backward propagation, or backpropagation, is the process of using this error to move backward through the network and adjust the neuron weights.

By updating the weights in this way, the network gradually reduces its error and improves the accuracy of its predictions.

Note

The neural network error can be calculated in different ways depending on the task, but it is always a floating point number.

1. What is forward propagation in a neural network?

2. What is backpropagation in a neural network?

3. When training a neural network, what happens after forward propagation stage?

What is forward propagation in a neural network?

Select the correct answer

This is the process of training a neural network.

This is the process by which information passes through the network from the input layer to the output layer.

This is the process in which the neural network handles the error after the prediction.

This is the process of setting the initial weights on the network.

What is backpropagation in a neural network?

Select the correct answer

This is the process in which the neural network calculates the error after a prediction and uses this information to adjust the weights.

This is the process of setting the initial weights on the network.

This is the process by which information passes through the network from the input layer to the output layer.

This is not a part of the neural network training process.

When training a neural network, what happens after forward propagation stage?

Select the correct answer

The neural network starts a new forward propagation cycle.

The forecast is compared with real data and the error is calculated.

The weights in the network are adjusted regardless of the error.

The network stops running because the learning process is complete.

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 7