Deep Learning CNN Convolutional Neural Networks with Python - VGG

The video tutorial discusses the VGG convolutional neural network, which was introduced in 2014. It explains the structure of VGG blocks, consisting of multiple convolutional layers followed by a max pooling layer. The tutorial details the architecture of the VGG network, including the number of layers and channels in each block. It also covers the VGG 11, 16, and 19 models, highlighting their differences and features. The concept of spatial dimension reduction and the influence of VGG on subsequent networks like ResNet and Inception is also discussed.

Explain how the number of output channels changes in the VGG network.

How many total layers does the VGG-11 network consist of?

What are the dimensions of the input tensor after passing through the first two VGG blocks?