Deep Learning Convolutional Neural Networks 3

Question 1

What is data augmentation used for when training CNNs?

Accepted Answer

Artificially expanding training data with transformations to improve generalization

Answer

Data augmentation applies random transforms like flips, crops, and color jitter to training images, exposing the model to more variation and reducing overfitting.

Question 2

What does fine-tuning mean in the context of transfer learning with pretrained CNNs?

Accepted Answer

Continuing to train some or all pretrained layers on the new task with a low learning rate

Answer

Fine-tuning unfreezes pretrained layers and continues training them on the target dataset, adapting high-level features to the new domain.

Question 3

What is feature map in the context of a CNN?

Accepted Answer

The output activation volume produced by applying a set of filters to the previous layer

Answer

A feature map is the 2D activation output of a single convolutional filter applied across an input, representing the presence and strength of a detected pattern.

Question 4

Which CNN architecture was the first to demonstrate the power of deep learning on ImageNet, winning in 2012?

Accepted Answer

AlexNet

Answer

AlexNet, developed by Krizhevsky, Sutskever, and Hinton, won the 2012 ImageNet competition and sparked widespread interest in deep learning for computer vision.

Question 5

What is the vanishing gradient problem's equivalent for very deep CNNs without residual connections?

Accepted Answer

Gradient signal becoming too weak to update early layer weights effectively

Answer

In very deep CNNs without skip connections, gradients diminish as they propagate through many layers, making early layers learn very slowly or not at all.

Deep Learning Practice Test

Deep Learning Practice Test

Deep Learning Convolutional Neural Networks 3