Python for Deep Learning - Build Neural Networks in Python - Feed-Forward and Back Propagation Networks

The video tutorial explains two key methods of signal propagation in neural networks: feedforward and backpropagation. The feedforward network (FFNN) allows information to move in one direction, from input to output nodes, without forming cycles. In contrast, backpropagation is used when the desired output is not achieved, allowing the network to adjust weights by moving backward through the network. This process helps the network learn and improve its accuracy.

What is the primary characteristic of a feedforward network?

• Information moves in one direction only.

• It adjusts weights automatically.

• Information moves in multiple directions.

• It forms cycles within the network.

What happens if the feedforward network does not produce the desired result?

• The network is discarded.

• The network is retrained from scratch.

• The input data is changed.

• Backpropagation is used to adjust weights.

How does backpropagation help in improving the network's performance?

• By increasing the number of nodes.

• By adjusting the weights based on errors.

• By changing the input data.

• By forming cycles in the network.

Which of the following is NOT a feature of backpropagation?

• Adjusting weights to minimize error.

• Learning from the output errors.

• Forming loops in the network.

• Improving network accuracy over time.

What is the main goal of using backpropagation in neural networks?

• To form cycles in the network.

• To decrease the number of input nodes.

• To increase the number of hidden layers.

• To achieve the desired output by fine-tuning weights.