Learning Path
Question & Answer
Choose the Best Answer
It causes the model to overfit the training data too quickly.
Deeper networks can suffer from vanishing gradients, making training ineffective.
More layers always improve the model's capacity to learn.
Increased depth requires more data, which is not always available.
Understanding the Answer
Let's break down why this is correct
When a network has many layers, the error signal that tells the model how to change its weights travels through each layer during training. Other options are incorrect because People think more layers mean the model will fit the training data too fast, but that happens when the data is too small, not just because the network is deeper; It is tempting to think that more layers always give a better model, but deeper networks are harder to train.
Key Concepts
Degradation Problem in Deep Networks
medium level question
understand
Deep Dive: Degradation Problem in Deep Networks
Master the fundamentals
Definition
The degradation problem in deep networks refers to the phenomenon where increasing network depth leads to saturation and rapid degradation in accuracy, despite not being caused by overfitting. This challenge highlights the complexities of optimizing deep models and the need for innovative approaches to prevent performance degradation.
Topic Definition
The degradation problem in deep networks refers to the phenomenon where increasing network depth leads to saturation and rapid degradation in accuracy, despite not being caused by overfitting. This challenge highlights the complexities of optimizing deep models and the need for innovative approaches to prevent performance degradation.
Ready to Master More Topics?
Join thousands of students using Seekh's interactive learning platform to excel in their studies with personalized practice and detailed explanations.