Python for Deep Learning - Build Neural Networks in Python - Minimizing the Cost Function Using Backpropagation

The video tutorial explains the concept of predicted and actual values in a model, highlighting the importance of minimizing the error or cost function for accuracy. It introduces backpropagation as a key algorithm for adjusting weights in neural networks to enhance output accuracy. The tutorial concludes with a brief transition to the next lecture.

What is the term used to describe the difference between predicted and actual values in a model?

• Cost function

• Propagation function

• Neural function

• Weight function

What happens when the predicted value matches the actual value in a model?

• The weights are reset

• The model needs adjustment

• The error is maximized

• No further action is needed

Which approach is used to update weights in a neural network to reduce error?

• Error propagation

• Lateral propagation

• Backpropagation

• Forward propagation

What is the primary purpose of backpropagation in neural networks?

• To enhance data input

• To decrease the number of layers

• To fine-tune weight functions

• To increase the number of neurons

Backpropagation is most commonly used in which field?

• Chemistry

• Biology

• Physics

• Artificial Intelligence