Vanishing and Exploding Gradient in Neural Networks

What term is commonly used to refer to the problem of unstable gradients in neural networks?

Exploding gradient dilemma
Vanishing gradient problem (correct)
Unstable weight conundrum
Fluctuating loss issue

How is the gradient typically calculated in a neural network?

Manually by the network architect
Through forward propagation
Using convolutional layers
By applying backpropagation (correct)

What is the purpose of updating the weights in a neural network with the gradient?

To find the most optimal weights for minimizing total loss (correct)
To slow down the training process
To maximize the total loss
To introduce randomness in the model

Which concept is primarily affected by the vanishing gradient problem in neural networks?

Weights of hidden layers (A) Signup and view all the answers

What problem arises when multiplying terms greater than one in deep learning?

Exploding gradient (C) Signup and view all the answers

Where in the network does the exploding gradient problem predominantly occur?

Early layers (D) Signup and view all the answers

How does the vanishing gradient problem differ from the exploding gradient problem?

Vanishing gradient decreases gradient size, exploding increases gradient size (B) Signup and view all the answers

What effect does an exploding gradient have on weight updates during training?

It greatly moves the weights (B) Signup and view all the answers

Why does an exploding gradient lead to weights moving too far from their optimal values?

Due to a large proportionate weight update (C) Signup and view all the answers

In which case will increasing the number of large-valued terms being multiplied have a significant impact on the gradient size?

When weights are large (C) Signup and view all the answers

What is the main issue caused by the vanishing gradient problem?

Weights in earlier layers of the network become stuck and do not update effectively. (D) Signup and view all the answers

How does the vanishing gradient problem relate to weight updates?

Small gradients lead to small weight updates that hinder network learning. (A) Signup and view all the answers

Why do earlier weights in the network face the vanishing gradient problem more severely?

Earlier weights require multiplying more small terms in the gradient calculation. (A) Signup and view all the answers

What happens if the terms involved in calculating a weight's gradient are 'small'?

The product of these terms becomes even smaller, affecting weight updates. (D) Signup and view all the answers

How does a small gradient affect weight updating in a neural network?

It leads to negligible weight changes that hinder overall learning. (B) Signup and view all the answers

Why does updating a weight with a small value further exacerbate the vanishing gradient problem?

Multiplying the small gradient by a small learning rate yields an even smaller update. (A) Signup and view all the answers

Why is it important for weights in a neural network to update sufficiently?

To help in minimizing the loss function effectively. (C) Signup and view all the answers

How does a vanishing gradient impact the performance of a neural network?

It hinders the ability of the network to learn effectively due to stuck weights. (C) Signup and view all the answers

What consequence arises from weights being 'stuck' due to vanishing gradients?

The weights fail to converge to optimal values, impairing network performance. (C) Signup and view all the answers

How does updating a stuck weight with a very small value typically affect network learning?

The weight remains stagnant, impeding learning progress throughout the network. (D) Signup and view all the answers

Why do earlier weights have more difficulty overcoming vanishing gradients compared to later ones?

The product of several small terms in earlier layers compounds, leading to smaller overall gradients. (B) Signup and view all the answers