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 are inception blocks and how many does Google Net use?

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What is the significance of the capital 'L' in Google Net?

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Describe the architecture of Google Net.

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Explain the problems associated with increasing the number of layers in convolutional neural networks.

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What are the vanishing and exploding gradient problems in deep learning?

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What is the role of residual blocks in neural networks?

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How does ResNet improve upon the issues faced by earlier architectures like Google Net?

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