Deep Learning CNN Convolutional Neural Networks with Python - GoogLeNet

The video tutorial discusses the architecture of GoogleNet, which utilizes inception blocks and Max pooling to reduce dimensionality. It explains the structure of GoogleNet, including its layers and filters. The tutorial also highlights challenges faced by deep networks, such as vanishing and exploding gradients, and introduces ResNet as a solution to these issues. ResNet's use of residual blocks helps improve training performance in deeper networks. The video concludes with a brief mention of Google A0's transition from inception net to ResNet.

Discuss the impact of training error on the performance of convolutional neural networks.

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What is the role of residual blocks in addressing issues in deep neural networks?

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How does ResNet improve upon the performance of earlier architectures like GoogLeNet?

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