Fundamentals of Neural Networks - Backward Propagation

The video tutorial covers the basics of neural networks, focusing on the flow of information from input to output layers. It introduces backward propagation, explaining the gradient descent algorithm used for optimization. The tutorial discusses the loss function, particularly mean square error, and draws an analogy to ordinary least squares (OLS) in linear regression. It details the steps of gradient descent, emphasizing the importance of the learning rate (ETA) and the challenges of exploding and vanishing gradients. The tutorial aims to provide a foundational understanding of these concepts for effective neural network training.

What is the primary purpose of backward propagation in neural networks?

• To increase the complexity of the model

• To propagate information from input to output

• To initialize the weights of the network

• To update weights to minimize the loss function

Which of the following is a common loss function used in backward propagation?

• Hinge loss

• Logarithmic loss

• Cross-entropy loss

• Mean square error

What is the role of the gradient in the gradient descent algorithm?

• To update the weights by indicating the direction of steepest descent

• To update the weights by indicating the direction of steepest ascent

• To calculate the loss function

• To determine the learning rate

What does the learning rate (ETA) control in the training of a neural network?

• The initial values of the weights

• The number of layers in the network

• The size of the steps taken towards the optimal point

• The type of activation function used

What problem arises if the learning rate is set too high?

• The model may oscillate and fail to converge

• The model may converge too quickly

• The model may underfit the data

• The model may overfit the data

What is a vanishing gradient problem?

• When the gradient becomes too large

• When the model has too many layers

• When the gradient becomes too small

• When the learning rate is too high

How can the issues of exploding and vanishing gradients be mitigated?

• By adding more layers to the network

• By adjusting the learning rate

• By increasing the number of neurons

• By using a different activation function