Deep Learning Lecture 7: Neuron Gradients and Vanishing Gradient Problem
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Questions and Answers

What issue do saturated neurons cause in deep learning?

Saturated neurons cause the gradient to vanish.

Why does the vanishing gradient problem restrict the possible update directions?

Saturated neurons cause the gradient to vanish.

What is the impact of non-zero centered sigmoid activation functions on backpropagation?

Sigmoids are not zero centered.

In which quadrant is it not possible for all gradients to be negative?

<p>Quadrant in which all gradients are -ve (Not possible).</p> Signup and view all the answers

What is the consequence of having saturated neurons in terms of the gradients of the weights?

<p>All gradients at a layer are either all positive or all negative.</p> Signup and view all the answers

How do the characteristics of the sigmoid activation function impact weight initialization?

<p>Sigmoids are not zero centered.</p> Signup and view all the answers

Why do saturated neurons cause the gradient to vanish?

<p>Saturated neurons lead to very small gradients which can vanish during backpropagation.</p> Signup and view all the answers

What is the issue with sigmoid activation functions not being zero-centered?

<p>Non-zero-centered activation functions like sigmoid can lead to zigzag paths during optimization.</p> Signup and view all the answers

How does the vanishing gradient problem affect weight updates in deep learning?

<p>In the presence of vanishing gradients, weight updates become very small or negligible, hindering the learning process.</p> Signup and view all the answers

Explain why weight initialization is crucial in deep learning.

<p>Proper weight initialization helps in avoiding issues like vanishing gradients and accelerates convergence during training.</p> Signup and view all the answers

What is the significance of using appropriate activation functions in neural networks?

<p>Choosing the right activation functions can prevent issues like vanishing gradients and improve the overall performance of the model.</p> Signup and view all the answers

How does the backpropagation algorithm help in training neural networks?

<p>Backpropagation calculates the gradients of the loss function with respect to the weights, enabling the model to update weights and learn from the errors.</p> Signup and view all the answers

Why do saturated neurons cause the gradient to vanish?

<p>Because the gradient becomes close to zero, leading to no learning happening in the network.</p> Signup and view all the answers

Why is it a problem if the gradient vanishes during training?

<p>A vanishing gradient means the network stops learning, as the weights do not get updated effectively, hindering convergence.</p> Signup and view all the answers

How does the non-zero centered nature of sigmoids impact gradient descent?

<p>The non-zero centered nature of sigmoids can lead to biased gradients, affecting the convergence and training of neural networks.</p> Signup and view all the answers

What happens to the gradient if the common term in the gradient calculation is positive?

<p>Both gradients, ∇w1 and ∇w2, will be positive.</p> Signup and view all the answers

Why is weight initialization crucial in neural networks?

<p>Proper weight initialization helps prevent issues like vanishing or exploding gradients at the start of training.</p> Signup and view all the answers

How do vanishing gradients impact the training process?

<p>Vanishing gradients make it difficult for the network to learn deep hierarchical representations, affecting the training of deep neural networks.</p> Signup and view all the answers

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