Question 5Which ones of the following statements on Residual Networks are true? (Check all that apply.)

To determine which statements about Residual Networks (ResNets) are true, you would typically need to evaluate each given statement against the established principles and characteristics of Residual Networks. Here is a general overview of key concepts related to Residual Networks:

1. Residual Learning: ResNets are designed to learn residual functions with reference to the layer inputs, which helps in training deeper networks effectively.

2. Skip Connections: ResNets utilize skip connections that bypass one or more layers, aiding in the gradient flow during backpropagation.

3. Addressing Vanishing Gradient Problem: The architecture is specifically crafted to mitigate vanishing gradient issues that are common in very deep networks.

4. Batch Normalization: While not a requirement, ResNets commonly utilize batch normalization, which helps stabilize and accelerate training.

5. Performance on Image Tasks: ResNets have demonstrated improved performance on a variety of image recognition tasks, especially on large datasets like ImageNet.

To accurately check which of the statements provided in your question are true, you would review them in light of these observations regarding the properties of Residual Networks. If you provide the specific statements, I can help evaluate them accordingly.