Deep Learning - Deep Neural Network for Beginners Using Python - Vanishing Gradient Problem

The video tutorial explains the vanishing gradient problem in neural networks, particularly when using the sigmoid activation function. It describes how the gradient becomes very small at extreme values of the sigmoid function, leading to slow or ineffective learning during backpropagation. The tutorial discusses the impact of small derivatives on weight updates and the learning rate, highlighting the challenges in reaching the minima efficiently. The vanishing gradient problem can significantly hinder the training process, making it difficult for neural networks to learn effectively.

What is the vanishing gradient problem primarily associated with?

• The tanh function

• The sigmoid function

• The ReLU function

• The linear function

Why do small derivatives in neural networks pose a problem?

• They cause the network to overfit

• They result in minimal weight updates

• They lead to large weight updates

• They increase the learning rate

What is the effect of multiplying small derivative values in a neural network?

• It leads to a vanishing gradient

• It causes the network to diverge

• It increases the learning rate

• It results in large gradient values

What is a potential consequence of the vanishing gradient problem during training?

• Increased learning rate

• Faster convergence to the minima

• Immediate overfitting

• Slower convergence or failure to reach minima

What hint is given to find a solution to the vanishing gradient problem?

• Use a larger learning rate

• Avoid using the sigmoid function

• Increase the number of epochs

• Use a smaller batch size