What is the main challenge with training deep neural networks?

a) Vanishing and exploding gradients

b) Lack of training data

c) High-dimensional input data

d) High accuracy on training data

Answer:

a) Vanishing and exploding gradients

Explanation:

The vanishing and exploding gradient problem is one of the major challenges in training deep neural networks. This issue arises during backpropagation, where gradients used to update the network’s weights become very small (vanishing) or very large (exploding) as they propagate through the layers.

When gradients vanish, the network stops learning because the weights are not updated effectively. When gradients explode, they cause unstable updates, leading to divergence in training. Both issues hinder the ability of deep networks to converge and learn from data.

To address these challenges, techniques such as gradient clipping, proper initialization methods, and using advanced architectures like LSTM and GRU networks are employed to ensure stable training.

Reference:

Artificial Intelligence MCQ (Multiple Choice Questions)