Data Science and Machine Learning (Theory and Projects) A to Z - Classical CNNs: Google Net

The video discusses the architecture of GoogleNet, which uses inception blocks and max pooling to reduce dimensionality. It highlights the challenges faced by deep convolutional neural networks, such as increased training error and vanishing gradients. The video introduces ResNet as a solution to these problems, emphasizing its ability to maintain performance in deeper networks. The transition from inception net to ResNet in Google's systems is also mentioned.

What is the primary reason for the capital 'L' in GoogleNet?

• To differentiate from other nets

• To indicate a large network

• To honor the LeNet architecture

• To emphasize the importance of layers

How many inception blocks are used in GoogleNet?

• 11

• 9

• 7

• 5

What is the purpose of Max pooling in GoogleNet?

• To increase the number of layers

• To enhance the network's expressiveness

• To improve training speed

• To reduce dimensionality

What happens to the training error as the number of layers increases in a deep network?

• It decreases continuously

• It remains constant

• It improves significantly

• It initially decreases then worsens

Which problem is NOT typically associated with deep convolutional networks?

• Vanishing gradients

• Increased training error

• Exploding gradients

• Improved generalization

What is a key advantage of ResNet over traditional deep networks?

• It uses fewer layers

• It avoids vanishing and exploding gradients

• It is faster to train

• It requires less data

Which architecture did Google A0 use before adopting ResNet?

• DenseNet

• Inception Net

• AlexNet

• VGGNet